CN108108455A

CN108108455A - Method for pushing, device, storage medium and the electronic equipment of destination

Info

Publication number: CN108108455A
Application number: CN201711461519.XA
Authority: CN
Inventors: 陈岩; 刘耀勇
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2017-12-28
Filing date: 2017-12-28
Publication date: 2018-06-01
Anticipated expiration: 2037-12-28
Also published as: CN108108455B

Abstract

The embodiment of the present application discloses a kind of method for pushing of destination, device, storage medium and electronic equipment, wherein, the method for pushing is included when detecting that user determines destination, and the corresponding multidimensional characteristic in acquisition destination builds the corresponding sample set in destination as sample；Sample classification is carried out to sample set for the information gain of sample classification according to feature, to construct the decision-tree model of destination, the output of decision-tree model is corresponding destination；When detecting that user spreads out the map in application, gathering current corresponding multidimensional characteristic as forecast sample；Corresponding destination is predicted according to forecast sample and decision-tree model.The automatic push of destination are realized with this, improve the push accuracy rate of destination.

Description

Method for pushing, device, storage medium and the electronic equipment of destination

Technical field

This application involves fields of communication technology, and in particular to a kind of method for pushing of destination, device, storage medium and electricity Sub- equipment.

Background technology

At present, with the high speed development of terminal technology, among increasingly going deep into people’s lives such as smart mobile phone, user is past It is largely applied toward that can be installed on smart mobile phone, such as chat application, game application, map application.

Wherein, user spreads out the map in application, generally require manually to search or search for destination, waste user when Between, and operating process is comparatively laborious, although present map application has some simple destination push, the pin of push It is not high to property, it solves the above problems therefore, it is necessary to provide a kind of method.

The content of the invention

In view of this, the embodiment of the present application provides a kind of method for pushing of destination, device, storage medium and electronics and sets It is standby, the push accuracy rate of destination can be improved.

In a first aspect, a kind of method for pushing for the destination that the embodiment of the present application provides, including：

When detecting that user determines destination, the corresponding multidimensional characteristic in the destination is gathered as sample, and structure Build the corresponding sample set in the destination；

Sample classification is carried out to the sample set for the information gain of sample classification according to the feature, to construct The decision-tree model of destination is stated, the output of the decision-tree model is corresponding destination；

When detecting that user spreads out the map in application, gathering current corresponding multidimensional characteristic as forecast sample；

Corresponding destination is predicted according to the forecast sample and the decision-tree model.

Second aspect, a kind of pusher for the destination that the embodiment of the present application provides, including：

First collecting unit, for when detecting that user determines destination, gathering the corresponding multidimensional in the destination Feature builds the corresponding sample set in the destination as sample；

Construction unit, for carrying out sample point to the sample set for the information gain of sample classification according to the feature Class, to construct the decision-tree model of the destination, the output of the decision-tree model is corresponding destination；

Second collecting unit detects that user spreads out the map in application, gathering current corresponding multidimensional characteristic work for working as For forecast sample；

Predicting unit, for predicting corresponding destination according to the forecast sample and the decision-tree model.

The third aspect, storage medium provided by the embodiments of the present application, is stored thereon with computer program, when the computer When program is run on computers so that the computer performs the push side of the destination provided such as the application any embodiment Method.

Fourth aspect, electronic equipment provided by the embodiments of the present application, including processor and memory, the memory has meter Calculation machine program, which is characterized in that the processor is by calling the computer program, for performing such as any implementation of the application The method for pushing for the destination that example provides.

The embodiment of the present application is by the way that when detecting that user determines destination, the corresponding multidimensional characteristic in acquisition destination is made For sample, and build the corresponding sample set in destination；Sample is carried out to sample set for the information gain of sample classification according to feature This classification, to construct the decision-tree model of destination, the output of decision-tree model is corresponding destination；When detecting user It spreads out the map in application, gathering current corresponding multidimensional characteristic as forecast sample；It is pre- according to forecast sample and decision-tree model Measure corresponding destination.The automatic push of destination are realized with this, improve the push accuracy rate of destination.

Description of the drawings

To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment Attached drawing is briefly described, it should be apparent that, the accompanying drawings in the following description is only some embodiments of the present invention, for For those skilled in the art, without creative efforts, it can also be obtained according to these attached drawings other attached Figure.

Fig. 1 is the application scenarios schematic diagram of the method for pushing of destination provided by the embodiments of the present application.

Fig. 2 is a flow diagram of the method for pushing of destination provided by the embodiments of the present application.

Fig. 3 is a kind of schematic diagram of decision tree provided by the embodiments of the present application.

Fig. 4 is the schematic diagram of another decision tree provided by the embodiments of the present application.

Fig. 5 is another flow diagram of the method for pushing of destination provided by the embodiments of the present application.

Fig. 6 is a structure diagram of the pusher of destination provided by the embodiments of the present application.

Fig. 7 is another structure diagram of the pusher of destination provided by the embodiments of the present application.

Fig. 8 is a structure diagram of electronic equipment provided by the embodiments of the present application.

Fig. 9 is another structure diagram of electronic equipment provided by the embodiments of the present application.

Specific embodiment

Schema is refer to, wherein identical element numbers represent identical component, the principle of the application is to implement one It is illustrated in appropriate computing environment.The following description be based on illustrated the application specific embodiment, should not be by It is considered as limitation the application other specific embodiments not detailed herein.

In the following description, the specific embodiment of the application will be with reference to as the step performed by one or multi-section computer And symbol illustrates, unless otherwise stating clearly.Therefore, these steps and operation will have to mention for several times is performed by computer, this paper institutes The computer execution of finger includes by representing with the computer processing unit of the electronic signal of the data in a structuring pattern Operation.This operation is converted at the data or the position being maintained in the memory system of the computer, reconfigurable Or in addition change the running of the computer in a manner of known to the tester of this field.The data structure that the data are maintained For the provider location of the memory, there is the specific feature as defined in the data format.But the application principle is with above-mentioned text Word illustrates that be not represented as a kind of limitation, this field tester will appreciate that plurality of step as described below and behaviour Also may be implemented among hardware.

Term as used herein " module " can regard the software object to be performed in the arithmetic system as.It is as described herein Different components, module, engine and service can be regarded as the objective for implementation in the arithmetic system.And device as described herein and side Method can be implemented in a manner of software, can also be implemented certainly on hardware, within the application protection domain.

Term " first ", " second " and " the 3rd " in the application etc. is for distinguishing different objects rather than for retouching State particular order.In addition, term " comprising " and " having " and their any deformations, it is intended that cover non-exclusive include. Such as contain the step of process, method, system, product or the equipment of series of steps or module is not limited to list or Module, but some embodiments further include the step of not listing or module or some embodiments further include for these processes, Method, product or equipment intrinsic other steps or module.

Referenced herein " embodiment " is it is meant that a particular feature, structure, or characteristic described can wrap in conjunction with the embodiments It is contained at least one embodiment of the application.Each position in the description occur the phrase might not each mean it is identical Embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.Those skilled in the art explicitly and Implicitly understand, embodiment described herein can be combined with other embodiments.

The embodiment of the present application provides a kind of method for pushing of destination, and the executive agent of the method for pushing of the destination can be with It is the pusher of destination provided by the embodiments of the present application or is integrated with the electronic equipment of the pusher of the destination, Hardware may be employed in the pusher of the wherein destination or the mode of software is realized.Wherein, electronic equipment can be intelligence The equipment such as mobile phone, tablet computer, palm PC, laptop or desktop computer.

Referring to Fig. 1, Fig. 1 is the application scenarios schematic diagram of the method for pushing of destination provided by the embodiments of the present application, with Exemplified by the pusher of destination integrates in the electronic device, when electronic equipment detects that user determines destination, acquisition The corresponding multidimensional characteristic in destination builds the corresponding sample set in destination as sample；According to feature for sample classification Information gain carries out sample classification to sample set, to construct the decision-tree model of destination；When detecting that user spreads out the map In application, the current corresponding multidimensional characteristic of acquisition is as forecast sample；Correspondence is predicted according to forecast sample and decision-tree model Destination.

It specifically, can be in historical time section, when electronic equipment detects that user determines purpose such as shown in Fig. 1 During ground, the corresponding multidimensional characteristic (weather characteristics, initially period feature, feature etc.) in acquisition destination is used as sample, and structure Build the corresponding sample set in destination；According to feature (weather characteristics, initially period feature, feature etc.) for sample classification Information gain carries out sample classification to sample set, to construct the decision-tree model of destination；When detecting that user spreads out the map During using a, gather current corresponding multidimensional characteristic (weather characteristics, initially period feature, feature etc.) and be used as forecast sample, Corresponding destination is predicted according to forecast sample and decision-tree model.

Referring to Fig. 2, Fig. 2 is the flow diagram of the method for pushing of destination provided by the embodiments of the present application.The application The idiographic flow of the method for pushing for the destination that embodiment provides can be as follows：

201st, when detecting that user determines destination, the corresponding multidimensional characteristic in acquisition destination is as sample, and structure Build the corresponding sample set in destination.

Destination mentioned by the present embodiment spreads out the map using the destination inputted for user, it is big to be specifically as follows XX Tall building, XX stations, XX supermarkets etc., when electronic equipment detects that user has selected certain destination, when gathering the selected destination Corresponding multidimensional characteristic is as sample.

Wherein, which has the dimension of certain length, the corresponding characterization destination of the parameter in each of which dimension A kind of characteristic information, i.e., the multidimensional characteristic breath be made of multiple features.When the plurality of feature can be including selected destination Relevant characteristic information, such as：Current state of weather；The current period；Initially information etc..

Wherein, the sample set of destination can include multiple samples, and each sample includes the corresponding multidimensional characteristic in destination. In the sample set of destination, it can be included in historical time section, multiple samples of the destination of acquisition.Historical time section, example It such as can be 7 days, 14 days in the past.It is understood that the multi-dimensional feature data of the destination once gathered forms a sample This, multiple samples form sample set.

After sample set is formed, each sample in sample set can be marked, obtain the sample of each sample Label, since this implementation will be accomplished that the destination of prediction map application, the sample label marked includes various mesh Ground namely the different destination of sample class.

202nd, sample classification is carried out to sample set for the information gain of sample classification according to feature, to construct destination Decision-tree model.

The embodiment of the present application can carry out sample classification for the information gain of sample classification with feature based to sample set, with Build the decision-tree model of destination.For example, can decision-tree model be built based on ID3 algorithms.

Wherein, decision tree is a kind of a kind of tree relied on decision-making and set up.In machine learning, decision tree is a kind of Prediction model, representative is a kind of a kind of mapping relations between object properties and object value, some is right for each node on behalf As, each diverging paths in tree represent some possible property value, and each leaf node then correspond to from root node to The value for the object represented by path that the leaf node is undergone.Decision tree only has single output, can be with if there is multiple outputs Independent decision tree is established respectively to handle different output.

Wherein, ID3 (Iterative Dichotomiser 3,3 generation of iteration binary tree) algorithm is one kind of decision tree, it It is based on "ockham's razor" principle, i.e., does more things with less thing with trying one's best.In information theory, it is expected that information is got over It is small, then information gain is bigger, so as to which purity is higher.The core concept of ID3 algorithms is exactly to be belonged to information gain to measure Property selection, the attribute of information gain maximum is into line splitting after selection division.The algorithm uses top-down greedy search time Go through possible decision space.

Wherein, information gain exactly sees a feature t for feature one by one, and system has it and do not have It when information content be respectively how many, the difference of the two is exactly the information content that this feature is brought to system, i.e. information gain.

The process classified based on information gain to sample set is described in detail below, for example, assorting process can wrap Include following steps：

Corresponding root node is generated, and using sample set as the nodal information of root node；

The sample set of root node is determined as current target sample collection to be sorted；

Obtain the information gain that feature is classified for sample set in target sample collection；

Current division feature is chosen from feature according to information gain selection；

Sample set is divided according to division feature, obtains several subsample collection；

The division feature of sample in sub- sample set is removed, subsample collection after being removed

The child node of present node is generated, and using subsample collection after removal as the nodal information of child node；

Judge whether child node meets default classification end condition；

If it is not, target sample collection then is updated to subsample collection after removing, and returns to execution and obtain spy in target sample collection Levy the information gain for sample set classification；

If so, using child node as leaf node, the classification for concentrating sample according to subsample after removal sets leaf section The output of point, the classification of sample is corresponding destination.

Wherein, division is characterized as the feature chosen according to the information gain that each feature is classified for sample set from feature, For classifying to sample set.Wherein, there are many modes that division feature is chosen according to information gain, such as in order to promote sample point The accuracy of class, can choose maximum information gain it is corresponding be characterized as division feature.

Wherein, the classification of sample is corresponding multiple destination classifications.

When child node meets default classification end condition, can it stop to the son using child node as leaf node The sample set classification of node, and can concentrate the classification of sample that the output of the leaf node is set based on subsample after removal. Classification based on sample is set there are many modes of the output of leaf node.It for example, can be by sample number in sample set after removal Measure output of most classifications as the leaf node.

Wherein, presetting classification end condition can set according to actual demand, and child node meets default classification and terminates item During part, using current node as leaf node, stop carrying out participle classification to the corresponding sample set of child node；Child node is not When meeting default classification end condition, continue to classify to the corresponding volume sample set of child node.For example, default classification terminates item Part can include：The categorical measure of sample is " to judge with default quantity namely step in the set of subsample after the removal of child node Whether child node meets default classification end condition " it can include：

Subsample concentrates whether the categorical measure of sample is default quantity after judging the corresponding removal of child node；

If so, determine that child node meets default classification end condition；

If not, it is determined that child node is discontented with default classified terminal end condition.

For example, default classification end condition can include：The classification of sample is concentrated in subsample after the corresponding removal of child node Quantity be 1 namely the sample set of child node in only there are one classification sample.At this point, if child node meets the default classification End condition, then, the classification of sample is concentrated into as the output of the leaf node in subsample.Subsample is concentrated only after such as removing When having the sample that classification is " XX mansions ", then, it can be by the output of " XX mansions " as the leaf node.

In one embodiment, in order to promote the accuracy of determination of decision-tree model, a gain threshold can also be set；When When maximum information gain is more than the threshold value, just choose the information gain for feature be division feature.That is, step " root Current division feature is chosen from feature according to information gain selection " it can include：

Maximum target information gain is chosen from information gain；

Judge whether target information gain is more than predetermined threshold value；

If so, the corresponding feature of target information gain is chosen as current division feature.

It in one embodiment, can be using present node as leaf section when target information gain is not more than predetermined threshold value Point, and choose output of the most sample class of sample size as the leaf node.Wherein, sample class is corresponding purpose Ground.

Wherein, predetermined threshold value can be set according to actual demand, such as 0.9,0.8.

For example, when feature 1 for sample classification information gain 0.9 be maximum information gain when, predetermined threshold value 0.8 When, since maximum information gain is more than predetermined threshold value, at this point it is possible to using feature 1 as division feature.

In another example when predetermined threshold value is 1, then maximum information gain is less than predetermined threshold value, at this point it is possible to will work as prosthomere Point understands sample set analysis classification is most for the sample size at " XX stations ", is " XX more than other classifications as leaf node The sample size of mansion ", at this point it is possible to by the output of " XX stations " as the leaf node.

Wherein, sample is carried out there are many modes of classifying and dividing according to division feature, for example, division feature can be based on Characteristic value sample set divided.Namely step " being divided according to division feature to sample set " can include：

Obtain the characteristic value that feature is divided in sample set；

Sample set is divided according to characteristic value.

It is concentrated for example, can will divide the identical sample of characteristic value in sample set and be divided into same subsample.For example, divide The characteristic value of feature includes：0th, 1,2, then at this point it is possible to the sample that the characteristic value for dividing feature is 0 be classified as it is a kind of, by feature The sample being worth for 1 is classified as sample that is a kind of, being 2 by characteristic value and is classified as one kind.

For example, for sample set A { sample 1, sample 2 ... sample i ... samples n }, wherein sample 1 includes feature 1, spy Sign 2 ... feature m, sample i include feature 1, feature 2 ... feature m, sample n include feature 1, feature 2 ... feature m.

First, all samples in sample set are initialized, then, generate a root node a, and using sample set as The nodal information of root node a, such as with reference to figure 3.

Calculate each feature information gain g1, g2 ... gm that for example feature 1, feature 2 ... feature m are classified for sample set； Maximum information gain gmax is chosen, if gi is maximum information gain.

When maximum information gain gmax is less than predetermined threshold value ε, current node chooses sample number as leaf node Measure output of most sample class as leaf node.

When maximum information gain gmax is more than predetermined threshold value ε, the corresponding feature i of information gain gmax can be chosen and made To divide feature t, sample set A { sample 1, sample 2 ... sample i ... samples n } is divided according to feature i, such as by sample This collection is divided into two sub- sample set A1 { sample 1, sample 2 ... sample k } and A2 { sample k+1 ... samples n }.

Feature t removal will be divided in subsample collection A1 and A2, at this point, in subsample collection A1 and A2 sample include feature 1, Feature 2 ... feature i-1, feature i+1 ... features n }.Generate the child node a1 and a2 of root node a with reference to figure 3, and by increment This collection A1 as the nodal information of child node a1, using subsample collection A2 as the nodal information of child node a2.

Then, for each child node, by taking child node a1 as an example, judge whether child node meets default classification and terminate item Part, if so, using current child node a1 as leaf node, and according to the class of the corresponding subsample concentration samples of child node a1 The leaf node is not set to export.

When child node is unsatisfactory for default classification end condition, by the way of the above-mentioned classification based on information gain, continue Classify to the corresponding subsample collection of child node, can such as be calculated by taking child node a2 as an example in A2 sample sets each feature compared with The information gain g of sample classification chooses maximum information gain gmax, when maximum information gain gmax is more than predetermined threshold value ε When, can choose information gain gmax it is corresponding be characterized as division feature t, based on division feature t A2 is divided into several sons A2 can be such as divided into subsample collection A21, A22, A23 by sample set, then, by the division in subsample collection A21, A22, A23 Feature t is removed, and generates child node a21, a22, a23 of present node a2, will remove the sample set A21 after division feature t, A22, A23 are respectively as the nodal information of child node a21, a22, a23.

And so on, decision tree as shown in Figure 4 is may be constructed out using the above-mentioned mode based on information gain classification, The output of the leaf node of the decision tree includes " XX destinations ".

In the embodiment of the present application, empirical entropy that can be based on sample classification and feature are for the item of sample set classification results Part entropy obtains the information gain that feature is classified for sample set.Namely " feature is for sample set in acquisition target sample collection for step The information gain of classification " can include：

Obtain the empirical entropy of sample classification；

Obtain conditional entropy of the feature for sample set classification results；

According to conditional entropy and empirical entropy, the information gain that feature is classified for sample set is obtained.

Wherein it is possible to obtain the probability that each destination sample occurs in sample set, which is sample class Not Wei corresponding destination, according to each probability obtain sample empirical entropy.

For example, for sample set Y { sample 1, sample 2 ... sample i ... samples n }, if sample class is XX mansions The sample size of sample is j, and the sample size of XX stations sample is n-j；At this point, appearance of the XX mansions sample in sample set Y Probability of occurrence p2=n-j/n of Probability p 1=j/n, the XX station in sample set Y.Then, the calculating based on following empirical entropy is public Formula calculates the empirical entropy H (Y) of sample classification：

Wherein, pi is probability of occurrence of the sample in sample set Y.In decision tree classification problem, information gain is exactly certainly The difference of plan tree information before Attributions selection division is carried out and after division.

In one embodiment, can sample set be divided by several subsample collection according to feature t, then, obtains each increment The probability that the comentropy of this collection classification and each characteristic value of this feature t occur in sample set, according to the comentropy and is somebody's turn to do Probability can be divided after comentropy, i.e., this feature t is for the conditional entropy of sample set classification results.

For example, for sample characteristics X, sample characteristics X can be by following for the conditional entropy of sample set Y classification results Formula is calculated：

Wherein, n is characterized the value kind number of X, i.e. characteristic value number of types.At this point, it is i-th kind of value that pi, which is X characteristic values, The probability that occurs in sample set Y of sample, xi is i-th kind of value of X.H (Y | X=xi) it is the experience that subsample collection Yi classifies Entropy, the X characteristic values of sample are i-th kind of value in the collection i of the subsample.

For example, using the value kind number of feature X as 3, i.e., exemplified by x1, x2, x3, at this point it is possible to which feature X is by sample set Y { samples 1st, sample 2 ... sample i ... samples n } three sub- sample sets are divided into, characteristic value is Y1 { sample 1, sample 2 ... the sample of x1 This d }, the Y2 { sample d+1 ... samples e } that characteristic value is x2, the Y3 { sample e+1 ... samples n } that characteristic value is x3.D, e is equal For positive integer, and less than n.

At this point, feature X is for the conditional entropy of sample set Y classification results：

H (Y | X)=p1H (Y | x1)+p2H (Y | x2)+p3H (Y | x3)；

Wherein, p1=Y1/Y, p2=Y2/Y, p2=Y3/Y；

H (Y | x1) it is the comentropy that subsample collection Y1 classifies, i.e. empirical entropy, the calculation formula of above-mentioned empirical entropy can be passed through It is calculated.

Obtaining the conditional entropy H (Y | X) of the empirical entropy H (Y) and feature X of sample classification for sample set Y classification results Afterwards, the information gain that feature X classifies for sample set Y can be calculated, is such as calculated by the following formula：

G (Y, X)=H (Y)-H (Y | X)

Namely feature X is for the sample set Y information gains classified：Empirical entropy H (Y) and feature X classifies for sample set Y As a result the difference of conditional entropy H (Y | X).

203rd, when detecting that user spreads out the map in application, gathering current corresponding multidimensional characteristic as forecast sample.

Wherein, when detect user spread out the map in application, illustrate user need carry out destination lookup.It is corresponding, it adopts The current corresponding multidimensional characteristic of collection is as default sample.

It should be strongly noted that in the embodiment of the present application, the multidimensional characteristic gathered in step 201 and 203 is identical spy Sign, such as：Current state of weather；The current period；Initially information etc..

204th, corresponding destination is predicted according to forecast sample and decision-tree model.

Specifically, corresponding output is obtained according to forecast sample and decision-tree model as a result, being determined pair according to output result The destination answered.Wherein, exporting result includes each destination.

For example, can corresponding leaf node be determined according to the feature and decision-tree model of forecast sample, by the leaf section The output of point is as prediction output result.Such as (feature is divided according to the branch condition of decision tree using the feature of forecast sample Characteristic value) determine current leaf node, take the leaf node output as predict result.It is defeated due to leaf node Go out including multiple destinations.

For example, after gathering current multidimensional characteristic, it can be with the branch condition according to decision tree in decision tree shown in Fig. 4 Corresponding leaf node is searched as an1, the output of leaf node an1 is destination 1, at this point, the destination for just determining push is Destination 1.

From the foregoing, it will be observed that the embodiment of the present application is by the way that when detecting that user determines destination, acquisition destination is corresponding Multidimensional characteristic builds the corresponding sample set in destination as sample；According to feature for sample classification information gain to sample This collection carries out sample classification, and to construct the decision-tree model of destination, the output of decision-tree model is corresponding destination；When Detect that user spreads out the map in application, gathering current corresponding multidimensional characteristic as forecast sample；According to forecast sample and certainly Plan tree-model predicts corresponding destination.The automatic push of destination are realized with this, the push for improving destination is accurate Rate.

Further, due in each sample of sample set, including reflection user the behavior of destination is selected to practise usually Used multiple characteristic informations, therefore the embodiment of the present application can so that the push to destination is more personalized and intelligent.

Further, realize that the push of destination is predicted based on decision tree prediction model, destination push can be promoted Accuracy, more be bonded user use habit.

On the basis of the method that will be described below in above-described embodiment, the sorting technique of the application is described further.Ginseng Fig. 5 is examined, the method for pushing of the destination can include：

301st, when detecting that user determines destination, the corresponding multidimensional characteristic in acquisition destination is as sample, and structure Build the corresponding sample set in destination.

Wherein, when detecting that user inputs destination by map application, when inputting " XX mansions " such as user, acquisition choosing Corresponding multidimensional characteristic is as sample during the fixed destination.

The multidimensional characteristic information of application has the dimension of certain length, the corresponding characterization purpose of the parameter in each of which dimension A kind of characteristic information on ground, i.e. the multidimensional characteristic information are made of multiple characteristic informations.The plurality of characteristic information can include choosing Determine relevant characteristic information during destination, such as：Current state of weather；The current period；Initially information, date letter Breath etc..

In the sample set of destination, multiple samples of acquisition in historical time section can be included in.Historical time section, such as It can be 7 days, 14 days etc. in the past.It is understood that the multi-dimensional feature data for once gathering destination forms a sample, it is more A sample forms sample set.

One specific sample can be as shown in table 1 below, and the characteristic information including multiple dimensions is, it is necessary to explanation, 1 institute of table The characteristic information shown is only for example, and in practice, the quantity for the characteristic information that a sample is included can be more than than shown in table 1 The quantity of information can also be less than the quantity of information shown in table 1, and the specific features information taken can also be different from shown in table 1, It is not especially limited herein.

Dimension	Characteristic information
		1	Current state of weather
2	The current period
		3	Initially information
4	Date information
		5	Current wireless network state, such as wifi connection status

Table 1

302nd, the sample in sample set is marked, obtains the sample label of each sample.

Since this implementation will be accomplished that prediction destination, the sample label marked includes each destination.It should The sample label of sample characterizes the sample class of the sample.At this point, sample class can " XX mansions ", " XX stations " etc..

In one embodiment, the sample in sample set is marked in this, after obtaining the sample label of each sample, It further includes：

(1) detect in sample set with the presence or absence of the sample that the corresponding characteristic value of multidimensional characteristic is identical but destination classification is different This；

Wherein, when there are the corresponding characteristic value of multidimensional characteristic is completely the same, but sample label is inconsistent in sample set, i.e., The different sample of destination classification, such as state of weather current in sample 1, initially current period, information, date information And currently the corresponding characteristic information of wireless network state and sample 2 are completely the same, but 1 corresponding destination classification of sample is " XX mansions ", and 2 corresponding destination classification of sample is then judged to detecting that there are multidimensional characteristics in sample set for " XX stations " The sample that corresponding characteristic value is identical but destination classification is different performs step (2).

(2) the most sample of destination classification sample size is retained.

Wherein, due to the corresponding characteristic value of multidimensional characteristic is identical but different samples that destination classification is different can shadow Ring the decision tree structure in later stage, thus when detect the presence of that the corresponding characteristic value of multidimensional characteristic is identical but destination classification During different different samples, the most sample of wherein destination classification sample size is retained, other samples are then deleted, such as Destination classification is the sample that the sample size of " XX mansions " is more than that destination classification is " XX stations ", then retains destination classification For all samples of " XX mansions ".

In one embodiment, if different destinations classification sample size is consistent, time when being stored according to sample, Retain storage time and the immediate destination classification sample of current time.

303rd, the root node of decision-tree model is generated, and using sample set as the nodal information of root node.

For example, with reference to figure 3, for sample set A { sample 1, sample 2 ... sample i ... samples n }, can first generate certainly The root node a of plan tree, and using sample set A as the nodal information of root node a.

304th, determine sample set for current target sample collection to be sorted.

Namely the sample set of definite root node is as current target sample collection to be sorted.

305th, the information gain that each feature is classified for sample set in target sample collection is obtained, and determines that maximum information increases Benefit.

For example, for sample set A, each feature can be calculated as feature 1, feature 2 ... feature m classify for sample set Information gain g1, g2 ... gm；Choose maximum information gain gmax.

Wherein, following manner acquisition may be employed in the information gain that feature is classified for sample set：

Obtain the empirical entropy of sample classification；Obtain conditional entropy of the feature for sample set classification results；According to conditional entropy and Empirical entropy obtains the information gain that feature is classified for sample set.

For example, each destination classification can be obtained.

The probability that each destination sample occurs in sample set can be obtained, which is that sample class is pair The destination answered obtains the empirical entropy of sample according to each probability.

For example, there was only " XX mansions " and " exemplified by XX stations " by the sample class of destination sample, for sample set Y { sample 1, sample 2 ... sample i ... samples n }, if the sample size that sample class is " XX mansions " is j, " XX stations " Sample size be n-j；At this point, the probability of occurrence p1=j/n of " XX mansions " in sample set Y, " XX stations " is in sample set Y Probability of occurrence p2=n-j/n.Then, the calculation formula based on following empirical entropy calculates the empirical entropy H (Y) of sample classification：

In decision tree classification problem, information gain is exactly decision tree information after carrying out Attributions selection and dividing preceding and division Difference.

H (Y | X)=p1H (Y | x1)+p2H (Y | x2)+p3H (Y | x3)；

Wherein, p1=Y1/Y, p2=Y2/Y, p3=Y3/Y；

G (Y, X)=H (Y)-H (Y | X)

306th, judge whether maximum information gain is more than predetermined threshold value, if so, step 307 is performed, if it is not, then performing Step 313.

Such as, it can be determined that whether maximum information gain gmax is more than default threshold epsilon, which can be according to reality Border demand setting.

307th, the corresponding feature of maximum information gain is chosen as division feature, and according to the characteristic value of the division feature Sample set is divided, obtains several subsample collection.

For example, when the maximum corresponding features of information gain gmax are characterized i, it can be using selected characteristic i as division feature.

Specifically, can sample set be divided by several subsample collection, subsample according to the characteristic value kind number of division feature The quantity of collection is identical with characteristic value kind number.For example, the identical sample of characteristic value can will be divided in sample set is divided into same son In sample set.For example, dividing the characteristic value of feature includes：0th, 1,2, then at this point it is possible to the sample that the characteristic value for dividing feature is 0 Originally it is classified as sample that is a kind of, being 1 by characteristic value and is classified as sample that is a kind of, being 2 by characteristic value being classified as one kind.

308th, the division feature that subsample is concentrated sample removes, subsample collection after being removed.

For example, the value of division feature i there are two types of when, sample set A can be divided into A1 { sample 1, sample 2 ... sample This k } and A2 { sample k+1 ... samples n }.It is then possible to the division feature i in subsample collection A1 and A2 is removed.

309th, the child node of present node is generated, and using subsample collection after removal as the nodal information of corresponding child node.

Wherein, a sub- sample set corresponds to a child node.For example, with reference to figure 3 generate root node a child node a1 and A2, and using subsample collection A1 as the nodal information of child node a1, using subsample collection A2 as the nodal information of child node a2.

310th, judge whether the subsample collection of child node meets default classification end condition, if so, step 311 is performed, If it is not, then perform step 312.

Wherein, presetting classification end condition can set according to actual demand, and child node meets default classification and terminates item During part, using current node as leaf node, stop carrying out participle classification to the corresponding sample set of child node；Child node is not When meeting default classification end condition, continue to classify to the corresponding volume sample set of child node.For example, default classification terminates item Part can include：The categorical measure of sample is and default quantity in the set of subsample after the removal of child node.

For example, default classification end condition can include：The classification of sample is concentrated in subsample after the corresponding removal of child node Quantity be 1 namely the sample set of child node in only there are one classification sample.

The 311st, target sample collection is updated to the subsample collection of child node, and return and perform step 305.

312nd, using the child node as leaf node, and concentrate sample class that the leaf is set according to the subsample of child node The output of node.

At this point, if child node meets the default classification end condition, then, by the destination class of subsample concentration sample Output not as the leaf node.Such as remove after subsample concentrate only purposefully classification be " XX stations " sample when, that , can be by the output of " XX stations " as the leaf node

313rd, using present node as leaf node, and the most sample class of sample size is chosen as the leaf node Output.

Wherein, sample class includes each destination.

For example, in the subsample collection A1 classification of child node a1, if maximum information gain is small and predetermined threshold value, at this point, It can be using the most sample class of sample size in the collection A1 of subsample as the output of the leaf node.Such as the sample of " XX mansions " Quantity is most, then can be by the output of " XX mansions " as leaf node a1.

314th, after decision-tree model has been built, when detecting that user spreads out the map in application, gathering current corresponding more Dimensional feature is as forecast sample.

315th, corresponding destination is predicted according to forecast sample and decision-tree model.

For example, can corresponding leaf node be determined according to the feature and decision-tree model of forecast sample, by the leaf section The output of point is as prediction output result.Such as (feature is divided according to the branch condition of decision tree using the feature of forecast sample Characteristic value) determine current leaf node, take the leaf node output as predict result.It is defeated due to leaf node Go out including each destination, therefore, can determine to need the destination pushed based on decision tree at this time.

For example, after gathering current multidimensional characteristic, it can be with the branch condition according to decision tree in decision tree shown in Fig. 4 Corresponding leaf node is searched as an2, the output of leaf node an2 is destination 2, at this point, the destination for just determining push is Destination 2.

From the foregoing, it will be observed that the embodiment of the present application is by the way that when detecting that user determines destination, acquisition destination is corresponding Multidimensional characteristic builds the corresponding sample set in destination as sample；According to feature for sample classification information gain to sample This collection carries out sample classification, and to construct the decision-tree model of destination, the output of decision-tree model is corresponding destination；When Detect that user spreads out the map in application, gathering current corresponding multidimensional characteristic as forecast sample；According to forecast sample and certainly Plan tree-model predicts corresponding destination.The automatic push of destination are realized with this, the push for improving destination is accurate Rate..

A kind of pusher of destination is additionally provided in one embodiment.Referring to Fig. 6, Fig. 6 is the embodiment of the present application The structure diagram of the pusher of the destination of offer.Wherein the pusher of the destination is applied to electronic equipment, the mesh Ground pusher include the first collecting unit 401, construction unit 402, the second collecting unit 403 and predicting unit 404, It is as follows：

First collecting unit 401 is corresponding more for when detecting that user determines destination, gathering the destination Dimensional feature builds the corresponding sample set in the destination as sample；

Construction unit 402, for carrying out sample to the sample set for the information gain of sample classification according to the feature This classification, to construct the decision-tree model of the destination, the output of the decision-tree model is corresponding destination；

Second collecting unit 403 detects that user spreads out the map in application, gathering current corresponding multidimensional characteristic for working as As forecast sample；

Predicting unit 404, for predicting corresponding destination according to the forecast sample and the decision-tree model.

In one embodiment, with reference to figure 7, construction unit 402 can include：

First node generates subelement 4021, for generating corresponding root node, and using the sample set as described The nodal information of node；The sample set of the root node is determined as current target sample collection to be sorted；

Gain obtains subelement 4022, increases for obtaining the feature in target sample collection for the information that sample set is classified Benefit；

Feature determination subelement 4023, for choosing current division from the feature according to described information gain selection Feature；

Classification subelement 4024 for being divided according to the division feature to the sample set, obtains several increments This collection；

Section point generates subelement 4025, and the division feature for concentrating sample to the subsample is gone It removes, subsample collection after being removed；The child node of present node is generated, and using subsample collection after the removal as the sub- section The nodal information of point；

Judgment sub-unit 4026, for judging whether child node meets default classification end condition, if it is not, then by the mesh Mark sample set is updated to subsample collection after the removal, and triggers the gain and obtain in subelement execution acquisition target sample collection The step of information gain that the feature is classified for sample set；If so, using the child node as leaf node, according to institute Subsample concentrates the classification of sample to set the output of the leaf node after stating removal, and the classification of the sample is corresponding purpose Ground.

Wherein, classification subelement 4024 can be used for obtaining in the sample set and divide the characteristic value of feature；

The sample set is divided according to the characteristic value.Identical sample is divided into identical subsample collection.

Wherein, feature determination subelement 4023, can be used for：

Maximum target information gain is chosen from described information gain；

Judge whether the target information gain is more than predetermined threshold value；

If so, the corresponding feature of the target information gain is chosen as current division feature.

In one embodiment, gain obtains subelement 4022, can be used for：

Obtain the empirical entropy of sample classification；

According to the conditional entropy and the empirical entropy, the information gain that the feature is classified for the sample set is obtained.

For example, gain obtains subelement 4022, can be used for：Obtain each destination sample occur in sample set it is general Rate, the destination sample are that sample class is corresponding destination, and the empirical entropy of sample is obtained according to each probability.

In one embodiment, judgment sub-unit 4025 can be used for judging subsample after the corresponding removal of the child node Whether the categorical measure for concentrating sample is default quantity；

If so, determine that the child node meets default classification end condition.

In one embodiment, feature determination subelement 4023 can be also used for being not more than default threshold when target information gain During value, using present node as leaf node, and the most sample class of sample size is chosen as the defeated of the leaf node Go out.

In one embodiment, with reference to figure 7, the pusher of the destination further includes：

Detection unit 405, for detecting in the sample set with the presence or absence of the corresponding characteristic value of multidimensional characteristic is identical but mesh The different sample of ground classification.

Stick unit 406, for when detecting in the sample set the identical but mesh there are the corresponding characteristic value of multidimensional characteristic Ground classification different sample when, retain the most sample of destination classification sample size.

Wherein, the step of each unit performs in the pusher of destination may be referred to the side of above method embodiment description Method step.The pusher of the destination can integrate in the electronic device, such as mobile phone, tablet computer.

When it is implemented, Yi Shang unit can be independent entity realization, can also be combined, as Same or several entities realize, more than the specific implementation of each unit can be found in the embodiment of front, details are not described herein.

From the foregoing, it will be observed that the pusher of the present embodiment destination can be by detecting user when the first collecting unit 401 When destination is determined, the corresponding multidimensional characteristic in acquisition destination builds the corresponding sample set in destination as sample；Structure Unit 402 carries out sample classification for the information gain of sample classification according to feature to sample set, to construct determining for destination Plan tree-model, the output of decision-tree model is corresponding destination；It is answered when the second collecting unit 403 detects that user spreads out the map Used time gathers current corresponding multidimensional characteristic as forecast sample；Predicting unit 404 is pre- according to forecast sample and decision-tree model Measure corresponding destination.The automatic push of destination are realized with this, improve the push accuracy rate of destination.

The embodiment of the present application also provides a kind of electronic equipment.Referring to Fig. 8, electronic equipment 500 include processor 501 and Memory 502.Wherein, processor 501 is electrically connected with memory 502.

The processor 500 is the control centre of electronic equipment 500, is set using various interfaces and the entire electronics of connection Standby various pieces computer program in memory 502 and are called by operation or load store and are stored in memory Data in 502 perform the various functions of electronic equipment 500 and handle data, so as to carry out whole prison to electronic equipment 500 Control.

The memory 502 can be used for storage software program and module, and processor 501 is stored in memory by operation 502 computer program and module, so as to perform various functions application and data processing.Memory 502 can mainly include Storing program area and storage data field, wherein, storing program area can storage program area, the computer needed at least one function Program (such as sound-playing function, image player function etc.) etc.；Storage data field can be stored uses institute according to electronic equipment Data of establishment etc..In addition, memory 502 can include high-speed random access memory, non-volatile memories can also be included Device, for example, at least a disk memory, flush memory device or other volatile solid-state parts.Correspondingly, memory 502 can also include Memory Controller, to provide access of the processor 501 to memory 502.

In the embodiment of the present application, the processor 501 in electronic equipment 500 can be according to the steps, by one or one The corresponding instruction of process of a above computer program is loaded into memory 502, and is stored in by the operation of processor 501 Computer program in reservoir 502, it is as follows so as to fulfill various functions：

In some embodiments, the sample set is being carried out for the information gain of sample classification according to the feature Sample classification, during constructing the decision-tree model of the application, processor 501 can specifically perform following steps：

Corresponding root node is generated, and using the sample set as the nodal information of the root node；

The sample set of the root node is determined as current target sample collection to be sorted；

Obtain the information gain that the feature is classified for sample set in target sample collection；

Current division feature is chosen from the feature according to described information gain；

The sample set is divided according to the division feature, obtains several subsample collection；

The division feature for concentrating sample to the subsample is removed, subsample collection after being removed；

The child node of present node is generated, and using subsample collection after the removal as the nodal information of the child node；

Judge whether child node meets default classification end condition；

If it is not, the target sample collection then is updated to subsample collection after the removal, and returns to execution and obtain target sample The step of information gain that the feature is classified for sample set in this collection；

If so, using the child node as leaf node, the classification for concentrating sample according to subsample after the removal is set The output of the leaf node is put, the classification of the sample is corresponding destination.

In some embodiments, according to gathering the corresponding multidimensional characteristic in the destination as sample, and institute is built After stating the corresponding sample set in destination, processor 501 can also specifically perform following steps：

It detects in the sample set with the presence or absence of the sample that the corresponding characteristic value of multidimensional characteristic is identical but destination classification is different This；

When detecting samples identical there are the corresponding characteristic value of multidimensional characteristic in the sample set but that destination classification is different This when, retains the most sample of destination classification sample size.

In some embodiments, when choosing current division feature from the feature according to described information gain, Processor 501 can specifically perform following steps：

Maximum target information gain is chosen from described information gain；

In some embodiments, processor 501 can also specifically perform following steps：

When target information gain is not more than predetermined threshold value, using present node as leaf node, and sample size is chosen Output of most sample class as the leaf node.

In some embodiments, the information gain that the feature is classified for sample set in target sample collection is being obtained When, processor 501 can specifically perform following steps：

Obtain the empirical entropy of sample classification；

It can be seen from the above, the electronic equipment of the embodiment of the present application, by when detecting that user determines destination, gathering The corresponding multidimensional characteristic in destination builds the corresponding sample set in destination as sample；According to feature for sample classification Information gain carries out sample classification to sample set, and to construct the decision-tree model of destination, the output of decision-tree model is pair The destination answered；When detecting that user spreads out the map in application, gathering current corresponding multidimensional characteristic as forecast sample；According to Forecast sample and decision-tree model predict corresponding destination.The automatic push of destination are realized with this, improve purpose The push accuracy rate on ground.

Also referring to Fig. 9, in some embodiments, electronic equipment 500 can also include：Display 503, radio frequency electrical Road 504, voicefrequency circuit 505 and power supply 506.Wherein, wherein, display 503, radio circuit 504, voicefrequency circuit 505 and Power supply 506 is electrically connected respectively with processor 501.

The display 503 is displayed for by information input by user or is supplied to the information of user and various figures Shape user interface, these graphical user interface can be made of figure, text, icon, video and its any combination.Display 503 can include display panel, in some embodiments, liquid crystal display (Liquid Crystal may be employed Display, LCD) or the forms such as Organic Light Emitting Diode (Organic Light-Emitting Diode, OLED) match somebody with somebody Put display panel.

The radio circuit 504 can be used for transceiving radio frequency signal, to pass through wireless communication and the network equipment or other electricity Sub- equipment establishes wireless telecommunications, the receiving and transmitting signal between the network equipment or other electronic equipments.

The voicefrequency circuit 505 can be used for providing the audio between user and electronic equipment by loud speaker, microphone Interface.

The power supply 506 is used to all parts power supply of electronic equipment 500.In some embodiments, power supply 506 Can be logically contiguous by power-supply management system and processor 501, so as to realize management charging by power-supply management system, put The functions such as electricity and power managed.

Although not shown in Fig. 9, electronic equipment 500 can also include camera, bluetooth module etc., and details are not described herein.

The embodiment of the present application also provides a kind of storage medium, and the storage medium is stored with computer program, when the meter When calculation machine program is run on computers so that the computer performs the push side of the destination in any of the above-described embodiment Method, such as：When detecting that user determines destination, the corresponding multidimensional characteristic in the destination is gathered as sample, and structure Build the corresponding sample set in the destination；Sample is carried out to the sample set for the information gain of sample classification according to the feature This classification, to construct the decision-tree model of the destination, the output of the decision-tree model is corresponding destination；Work as inspection User is measured to spread out the map in application, gathering current corresponding multidimensional characteristic as forecast sample；According to the forecast sample and The decision-tree model predicts corresponding destination.

In the embodiment of the present application, storage medium can be magnetic disc, CD, read-only memory (Read Only Memory, ROM) or random access memory (Random Access Memory, RAM) etc..

In the above-described embodiments, all emphasize particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment Point, it may refer to the associated description of other embodiment.

It should be noted that for the method for pushing of the destination of the embodiment of the present application, this field common test personnel It is appreciated that realizing all or part of flow of the method for pushing of the destination of the embodiment of the present application, being can be by computer journey Sequence controls relevant hardware to complete, and the computer program can be stored in a computer read/write memory medium, such as deposited Storage performs in the memory of electronic equipment, and by least one processor in the electronic equipment, can wrap in the process of implementation Include the flow of the embodiment of the method for pushing such as destination.Wherein, the storage medium can be magnetic disc, CD, read-only storage Device, random access memory etc..

For the pusher of the destination of the embodiment of the present application, each function module can be integrated in a processing core In piece or modules are individually physically present, can also two or more modules be integrated in a module.On The form realization that hardware had both may be employed in integrated module is stated, can also be realized in the form of software function module.The collection If into module realized in the form of software function module and be independent production marketing or in use, can also be stored in In one computer read/write memory medium, the storage medium is for example read-only memory, disk or CD etc..

Method for pushing, device, storage medium and the electronics of a kind of destination provided above the embodiment of the present application are set Standby to be described in detail, the principle and implementation of this application are described for specific case used herein, more than The explanation of embodiment is only intended to help to understand the present processes and its core concept；Meanwhile for those skilled in the art Member, according to the thought of the application, there will be changes in specific embodiments and applications, in conclusion this explanation Book content should not be construed as the limitation to the application.

Claims

1. a kind of method for pushing of destination, which is characterized in that including：

When detecting that user determines destination, the corresponding multidimensional characteristic in the destination is gathered as sample, and builds institute State the corresponding sample set in destination；

Sample classification is carried out to the sample set for the information gain of sample classification according to the feature, to construct the mesh Ground decision-tree model, the output of the decision-tree model is corresponding destination；

2. the method for pushing of destination as described in claim 1, which is characterized in that according to the feature for sample classification Information gain carries out sample classification to the sample set, to construct the decision-tree model of the application, including：

Judge whether child node meets default classification end condition；

If it is not, the target sample collection then is updated to subsample collection after the removal, and returns to execution and obtain target sample collection The step of information gain that the interior feature is classified for sample set；

If so, using the child node as leaf node, the classification for concentrating sample according to subsample after the removal sets institute The output of leaf node is stated, the classification of the sample is corresponding destination.

3. the method for pushing of destination as claimed in claim 2, which is characterized in that the destination is corresponding more according to gathering Dimensional feature is further included as sample, and after building the corresponding sample set in the destination：

It detects in the sample set with the presence or absence of the sample that the corresponding characteristic value of multidimensional characteristic is identical but destination classification is different；

When detecting samples identical there are the corresponding characteristic value of multidimensional characteristic in the sample set but that destination classification is different, Retain the most sample of destination classification sample size.

4. the method for pushing of destination as claimed in claim 2, which is characterized in that according to described information gain from the feature It is middle to choose current division feature, including：

Maximum target information gain is chosen from described information gain；

5. the method for pushing of destination as claimed in claim 4, which is characterized in that the method for pushing of the destination also wraps It includes：

When target information gain is not more than predetermined threshold value, using present node as leaf node, and it is most to choose sample size Output of the sample class as the leaf node.

6. the method for pushing of destination as claimed in claim 2, which is characterized in that judge whether child node meets default classification End condition, including：

Subsample concentrates whether the categorical measure of sample is default quantity after judging the corresponding removal of the child node；

7. such as the method for pushing of claim 2-6 any one of them destination, which is characterized in that obtain institute in target sample collection The information gain that feature is classified for sample set is stated, including：

Obtain the empirical entropy of sample classification；

8. the method for pushing of destination as claimed in claim 7, which is characterized in that according to the conditional entropy and the experience Entropy obtains the information gain that the feature is classified for the sample set, including：

G (Y, X)=H (Y)-H (Y | X)

Wherein, g (Y, X) is characterized the information gain that X classifies for sample set Y, the empirical entropy that H (Y) classifies for sample set Y, H (Y | X) it is characterized conditional entropies of the X for sample set Y classification results.

9. the method for pushing of destination as claimed in claim 8, which is characterized in that the empirical entropy of sample classification is obtained, including：

The probability that each destination sample occurs in the sample set is obtained, the destination sample is that sample class is corresponding Destination；

The empirical entropy of the sample is obtained according to the probability.

10. a kind of pusher of destination, which is characterized in that including：

First collecting unit, for when detecting that user determines destination, gathering the corresponding multidimensional characteristic in the destination As sample, and build the corresponding sample set in the destination；

Construction unit, for carrying out sample classification to the sample set for the information gain of sample classification according to the feature, To construct the decision-tree model of the destination, the output of the decision-tree model is corresponding destination；

Second collecting unit detects that user spreads out the map in application, gathering current corresponding multidimensional characteristic as pre- for working as Test sample sheet；

11. the pusher of destination as claimed in claim 10, which is characterized in that the construction unit includes：

First node generates subelement, for generating corresponding root node, and using the sample set as the section of the root node Point information；The sample set of the root node is determined as current target sample collection to be sorted；

Gain obtains subelement, for obtaining the information gain that the feature is classified for sample set in target sample collection；

Feature determination subelement, for choosing current division feature from the feature according to described information gain selection；

Classification subelement for being divided according to the division feature to the sample set, obtains several subsample collection；

Section point generates subelement, and the division feature for concentrating sample to the subsample is removed, and is gone Except rear subsample collection；The child node of present node is generated, and using subsample collection after the removal as the node of the child node Information；

Judgment sub-unit, for judging whether child node meets default classification end condition, if it is not, then by the target sample collection Subsample collection after the removal is updated to, and triggers the gain and obtains subelement and perform and obtain the feature in target sample collection For sample set classification information gain the step of；If so, using the child node as leaf node, after the removal Subsample concentrates the classification of sample to set the output of the leaf node, and the classification of the sample is corresponding destination.

12. the pusher of destination as claimed in claim 11, which is characterized in that described device further includes：

Detection unit, for detecting in the sample set with the presence or absence of the corresponding characteristic value of multidimensional characteristic is identical but destination classification Different samples；

Stick unit, for when detecting in the sample set the identical but destination classification there are the corresponding characteristic value of multidimensional characteristic During different sample, retain the most sample of destination classification sample size.

13. the pusher of destination as claimed in claim 11, which is characterized in that feature determination subelement is used for：

Maximum target information gain is chosen from described information gain；

14. the pusher of destination as claimed in claim 11, which is characterized in that the gain obtains subelement, is used for：

Obtain the empirical entropy of sample classification；

15. a kind of storage medium, is stored thereon with computer program, which is characterized in that when the computer program is in computer During upper operation so that the computer performs the method for pushing of destination as described in any one of claim 1 to 9.

16. a kind of electronic equipment, including processor and memory, the memory has computer program, which is characterized in that described Processor is by calling the computer program, for performing the push side of destination as described in any one of claim 1 to 9 Method.