CN113657499B - Rights and interests distribution method and device based on feature selection, electronic equipment and medium - Google Patents
Rights and interests distribution method and device based on feature selection, electronic equipment and medium Download PDFInfo
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- CN113657499B CN113657499B CN202110944303.9A CN202110944303A CN113657499B CN 113657499 B CN113657499 B CN 113657499B CN 202110944303 A CN202110944303 A CN 202110944303A CN 113657499 B CN113657499 B CN 113657499B
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
- G06F18/2113—Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/245—Classification techniques relating to the decision surface
- G06F18/2451—Classification techniques relating to the decision surface linear, e.g. hyperplane
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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Abstract
The invention relates to an artificial intelligence technology, and discloses a rights and interests distribution method based on feature selection, which comprises the following steps: classifying the rights and interests data sets extracted according to the historical sales data sets to obtain a plurality of rights and interests data subsets, summarizing and calculating rights and interests matrixes corresponding to the rights and interests data subsets to obtain feature sets, selecting features of the feature sets to obtain a plurality of feature subsets, classifying the feature subsets by using a hyperplane function constructed according to the feature subsets to obtain classification results, comparing the classification results with classification labels, screening feature subsets consistent with the classification labels, selecting target rights and interests from the rights and interests data subsets according to the feature subsets, and distributing the target rights and interests to a plurality of users. In addition, the invention also relates to a blockchain technology, and the rights matrix can be stored in nodes of the blockchain. The invention also provides a rights and interests distribution device based on the feature selection, electronic equipment and a computer readable storage medium. The invention can solve the problem of lower accuracy of rights and interests distribution.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a method and apparatus for rights and interests allocation based on feature selection, an electronic device, and a computer readable storage medium.
Background
The current social insurance industry is increasingly competitive, particularly the car insurance is particularly competitive in the category of property insurance, so that the user viscosity can be increased by carrying out rights and interests allocation on the user, and the method is outstanding in the highly competitive car insurance.
In the prior art, the rights and interests are distributed evenly, however, because the rights and interests are limited in distribution and the number of participants involved in distribution is large, the distribution method easily causes that each participant is very little in distributed rights and interests, and the normal application of the rights and interests is influenced. Therefore, the existing equity distribution manner cannot accurately distribute, which results in unbalanced distribution, reduced equity utilization and increased equity resource waste rate.
Disclosure of Invention
The invention provides a feature selection-based rights and interests distribution method, device and computer readable storage medium, which mainly aim to solve the problem of lower accuracy of rights and interests distribution.
In order to achieve the above object, the present invention provides a rights and interests distribution method based on feature selection, including:
acquiring a historical sales data set, extracting a rights and interests data set in the historical sales data set, and classifying the rights and interests data set to obtain a plurality of rights and interests data subsets;
respectively calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain a feature set;
performing feature selection processing on the feature set to obtain a plurality of feature subsets;
constructing a hyperplane function according to the feature subsets, and classifying the feature subsets by utilizing the hyperplane function to obtain a classification result;
screening a feature subset consistent with the classification label by comparing the classification result with a preset classification label;
and selecting the target interests from the interest data subsets according to the feature subsets consistent with the classification labels, and distributing the target interests to a plurality of users according to preset distribution rules.
Optionally, the performing feature selection processing on the feature set to obtain a plurality of feature subsets includes:
screening a primary selected feature set meeting a preset range from the feature set;
And randomly arranging and combining the initially selected feature sets to obtain a plurality of feature subsets.
Optionally, the constructing a hyperplane function according to the feature subsets includes:
acquiring a preset label set, and taking the number of the feature subsets as feature dimensions;
constructing a multidimensional coordinate system consistent with the characteristic dimension according to the tag set and the characteristic dimension;
mapping the feature subsets into the multi-dimensional coordinate system to obtain feature coordinate sets;
calculating Euclidean distance between any two feature coordinates in the feature coordinate set, and selecting two feature coordinates with the minimum Euclidean distance as target feature coordinates;
and respectively taking the target feature coordinates as a left boundary and a right boundary, and constructing a hyperplane function in the middle of the left boundary and the right boundary.
Optionally, the classifying the feature subset by using the hyperplane function to obtain a classification result includes:
calculating a distance value from the hyperplane function to the target feature coordinates, and constructing a minimum distance function according to the distance value;
constructing constraint conditions, wherein the constraint conditions are that the distance from each coordinate to the hyperplane is greater than or equal to a minimum distance function;
Solving a minimum distance function based on the constraint condition by using a preset Lagrangian function to obtain a hyperplane;
and classifying the feature subsets according to the hyperplane to obtain a classification result.
Optionally, the calculating a distance value from the hyperplane function to the target feature coordinate includes:
calculating the distance value from the hyperplane function to the target feature coordinate according to a preset distance formula:
wherein, gamma i Is the distance value, x i For the ith target feature coordinate, y i And for the ith tag in the tag set, w and b are preset fixed parameters.
Optionally, the extracting the rights data set in the historical sales data set and classifying the rights data set to obtain a plurality of rights data subsets includes:
acquiring a preset rights and interests classification table, wherein the rights and interests classification table comprises a plurality of rights and interests types and rights and interests data corresponding to the rights and interests types;
and distributing the historical sales data set into the plurality of rights categories according to the rights classification table to obtain a plurality of rights data subsets.
Optionally, the calculating the right matrixes corresponding to the right data subsets respectively, and summarizing the right matrixes to obtain the feature set includes:
Analyzing pre-issuance data and actual usage data in the rights data subset;
substituting the pre-issued data and the actual use data into a preset rights formula for calculation to obtain a rights matrix corresponding to the rights data set;
and calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain the feature set.
In order to solve the above-mentioned problems, the present invention also provides a rights and interests distribution apparatus based on feature selection, the apparatus comprising:
the data classification module is used for acquiring a historical sales data set, extracting a rights and interests data set in the historical sales data set and classifying the rights and interests data set to obtain a plurality of rights and interests data subsets;
the matrix calculation module is used for calculating the right matrixes corresponding to the right data subsets respectively and summarizing the right matrixes to obtain a feature set;
the feature selection module is used for carrying out feature selection processing on the feature set to obtain a plurality of feature subsets;
the subset classification module is used for constructing a hyperplane function according to the feature subsets, classifying the feature subsets by utilizing the hyperplane function and obtaining a classification result;
The data screening module is used for screening a feature subset consistent with the classification label by comparing the classification result with a preset classification label;
and the rights and interests distribution module is used for selecting target rights and interests from the rights and interests data subsets according to the feature subsets consistent with the classification labels and distributing the target rights and interests to a plurality of users according to a preset distribution rule.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the instructions stored in the memory to realize the rights and interests allocation method based on the feature selection.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the above-mentioned feature selection-based rights allocation method.
In the embodiment of the invention, the rights and interests data sets in the historical sales data set are extracted and classified to obtain a plurality of rights and interests data sets, rights and interests data in the historical sales data set are deeply mined, rights and interests matrix corresponding to the rights and interests data sets are calculated respectively, the rights and interests matrix is summarized to obtain a feature set, feature selection processing is carried out on the feature set to obtain a plurality of feature subsets, diversity and richness of the feature subsets are guaranteed, a hyperplane function is constructed by the plurality of feature subsets, the feature subsets are classified by the hyperplane function, classification accuracy is improved, feature subsets consistent with the classification labels are screened out by comparing the classification results with preset classification labels, the classification labels are used as reference standards, screening accuracy is guaranteed, target rights and interests are selected from the rights and interests data subsets according to the feature subsets consistent with the classification labels, the target rights and interests data subsets are the benefits most suitable for users, and the target rights and interests are distributed to the plurality of users according to preset distribution rules. Therefore, the rights and interests distribution method, device, electronic equipment and computer readable storage medium based on the feature selection can solve the problem of lower accuracy of rights and interests distribution.
Drawings
FIG. 1 is a flow chart of a method for feature selection-based rights allocation according to an embodiment of the present application;
FIG. 2 is a functional block diagram of a rights and interests distribution apparatus based on feature selection according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device implementing the rights and interests allocation method based on feature selection according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a benefit distribution method based on feature selection. The execution subject of the rights allocation method based on feature selection includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the feature selection-based rights allocation method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a flow chart of a rights and interests distribution method based on feature selection according to an embodiment of the invention is shown. In this embodiment, the rights allocation method based on feature selection includes:
s1, acquiring a historical sales data set, extracting a rights and interests data set in the historical sales data set, and classifying the rights and interests data set to obtain a plurality of rights and interests data subsets.
In the embodiment of the invention, the historical sales data set is the historical service and marketing records of all clients in the historical service and marketing data of the insurance company.
Specifically, the extracting the rights data set in the historical sales data set and classifying the rights data set to obtain a plurality of rights data subsets includes:
acquiring a preset rights and interests classification table, wherein the rights and interests classification table comprises a plurality of rights and interests types and rights and interests data corresponding to the rights and interests types;
and distributing the historical sales data set into the plurality of rights categories according to the rights classification table to obtain a plurality of rights data subsets.
In detail, the rights classification table may include a plurality of rights categories and rights data corresponding to the plurality of rights categories.
For example, the plurality of rights categories include car washing rights, maintenance rights, proxy service rights, proxy driving service rights, detection adjustment rights, paint spraying rights, and sharing of basic knowledge science popularization links of car insurance. The rights data corresponding to the rights categories comprise relevant data such as car washing coupons issued to car insurance clients corresponding to car washing rights. And distributing the historical sales data set to the plurality of rights categories according to the rights classification table to obtain a plurality of rights data subsets, wherein the rights classification table comprises car washing rights, car washing coupons and other related data related to the car washing rights can be classified into one right of the car washing rights, and meanwhile, the car washing coupons and other related data related to the car washing rights are one of the rights data subsets.
S2, calculating the right matrixes corresponding to the right data subsets respectively, and summarizing the right matrixes to obtain a feature set.
In the embodiment of the present invention, the calculating the rights matrix corresponding to the plurality of rights data subsets, and summarizing the rights matrix to obtain the feature set includes:
analyzing pre-issuance data and actual usage data in the rights data subset;
substituting the pre-issued data and the actual use data into a preset rights formula for calculation to obtain a rights matrix corresponding to the rights data set;
and calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain the feature set.
In detail, the pre-distribution data refers to the distribution times of the equity of each category j in the equity data subset for each specific client i, and the sum of the actual use times under the actual use data line and the actual click times of the on-line link.
Specifically, the substituting the pre-issued data and the actual use data into a preset rights formula for calculation to obtain a rights matrix corresponding to the rights data set includes:
the preset equity formula is as follows:
Wherein x is ij To be a rights matrix, release ij For the pre-issue data, use ij For the actual usage data.
Further, each right data subset can be calculated to obtain a corresponding right matrix, and the right matrices corresponding to each right data subset are summarized to obtain a feature set.
And S3, performing feature selection processing on the feature set to obtain a plurality of feature subsets.
In the embodiment of the present invention, the improved LVW feature selection algorithm may be used for feature selection, where the improved LVW feature selection algorithm differs from the conventional LVW feature selection algorithm in that the conventional LVW feature selection algorithm may completely randomly select a feature subset, and a range may be preset in the scheme, and the feature subset may be selected and combined in the range.
Specifically, the feature selection processing is performed on the feature set to obtain a plurality of feature subsets, including:
screening a primary selected feature set meeting a preset range from the feature set;
and randomly arranging and combining the initially selected feature sets to obtain a plurality of feature subsets.
In detail, the preset range is beta epsilon [1, alpha ], the feature subset with 0 < alpha < omega dimension, wherein alpha is a preset value, and omega is the total dimension of the sample. And screening a primary selected feature set conforming to the preset range from the feature set, and randomly arranging and combining the primary selected features in the primary selected feature set to obtain a plurality of feature subsets.
For example, the feature set includes a feature a, a feature B, a feature C, a feature D, and a feature E, the initially selected feature set conforming to the preset range may be a feature a, a feature C, and a feature E, and the initially selected features in the initially selected feature set may be randomly arranged and combined, so that a plurality of feature subsets may be obtained, where the plurality of feature subsets are { feature a }, { feature C }, { feature E }, { feature a, feature C }, { feature a, feature E }, { feature C, feature E }, { feature a, feature C, and feature E }, respectively.
S4, constructing a hyperplane function according to the feature subsets, and classifying the feature subsets by using the hyperplane function to obtain a classification result.
In the embodiment of the present invention, the constructing a hyperplane function according to the feature subsets includes:
acquiring a preset label set, and taking the number of the feature subsets as feature dimensions;
constructing a multidimensional coordinate system consistent with the characteristic dimension according to the tag set and the characteristic dimension;
mapping the feature subsets into the multi-dimensional coordinate system to obtain feature coordinate sets;
calculating Euclidean distance between any two feature coordinates in the feature coordinate set, and selecting two feature coordinates with the minimum Euclidean distance as target feature coordinates;
And respectively taking the target feature coordinates as a left boundary and a right boundary, and constructing a hyperplane function in the middle of the left boundary and the right boundary.
In detail, in this solution, the tag set is a history tag of whether each client is actually renewing, for example, the category tag when being actually renewing is 1, and the category tag when not being actually renewing is 0. And if two feature subsets exist, the feature dimension is 2, the tag set is taken as the y axis, the feature subset is taken as the x axis to construct a two-dimensional coordinate system, and the feature subsets are mapped onto the two-dimensional coordinate system to obtain the feature coordinate set on the two-dimensional coordinate system. The target feature coordinates are respectively taken as a left boundary and a right boundary, the function of the left boundary can be w x+b=1, and the function of the right boundary can be w x+b= -1, so that the hyperplane function is w x+b=0.
Specifically, the classifying the feature subset by using the hyperplane function to obtain a classification result includes:
calculating a distance value from the hyperplane function to the target feature coordinates, and constructing a minimum distance function according to the distance value;
Constructing constraint conditions, wherein the constraint conditions are that the distance from each coordinate to the hyperplane is greater than or equal to a minimum distance function;
solving a minimum distance function based on the constraint condition by using a preset Lagrangian function to obtain a hyperplane;
and classifying the feature subsets according to the hyperplane to obtain a classification result.
Further, the calculating a distance value from the hyperplane function to the target feature coordinate includes:
calculating the distance value from the hyperplane function to the target feature coordinate according to a preset distance formula:
wherein, gamma i Is the distance value, x i For the ith target feature coordinate, y i And for the ith tag in the tag set, w and b are preset fixed parameters.
Specifically, the constructing a minimum distance function according to the distance value includes:
wherein gamma is a minimum distance function, gamma i Is a distance value.
In detail, a constraint condition is constructed, wherein the constraint condition is that the distance between each coordinate and the hyperplane is greater than or equal to a minimum distance function, and the constraint condition can be expressed as
Further, solving the minimum distance function based on the constraint condition by using a preset Lagrange function to obtain a hyperplane, wherein the method comprises the following steps:
Constructing the constraint condition and the minimum distance function into a Lagrangian objective function according to the Lagrangian function;
and solving the Lagrangian objective function to obtain a hyperplane.
In detail, the lagrangian objective function is:
wherein alpha is i For Lagrangian multiplier, w and b are preset fixed parameters, x i For the ith target feature coordinate, y i Is a label.
Specifically, the classification is performed on the feature subset according to the hyperplane, so as to obtain a classification result, for example, the hyperplane takes the feature subset { feature a }, { feature C } and { feature E } as a class, and the feature subset { feature a, feature C }, { feature a, feature E }, { feature C, feature E } and { feature a, feature C, feature E } as a class.
S5, comparing the classification result with a preset classification label, and screening a feature subset consistent with the classification label.
In the embodiment of the invention, the preset classification labels are labels with a plurality of rights and interests with maximum continuous preservation possibility, which are set in advance, and the feature subsets consistent with the classification labels are screened out by comparing the classification results with the preset classification labels.
In detail, the accuracy of the feature subset finally screened out can be ensured by comparing and screening.
S6, selecting target interests from the interest data subsets according to the feature subsets consistent with the classification labels, and distributing the target interests to a plurality of users according to a preset distribution rule.
In the embodiment of the invention, the feature subset consistent with the classification label is used as a reference, the target equity is selected from the equity data subset, and the target equity is distributed to a plurality of users according to a preset distribution rule, wherein the distribution rule can be distribution proportion of the target equity according to market research analysis.
For example, when the selected target equity is one, the target equity may be a maintenance equity, multiple users need to be screened and the maintenance equity is allocated to users meeting the screening requirement among the multiple users, and when the obtained target equity is multiple, the multiple target equity may be a paint spray equity, a car washing equity and a proxy service equity, and the target equity is allocated to the multiple users according to the allocation proportion obtained by the market research analysis according to the preset allocation rule. The distribution proportion obtained according to the market research analysis is the proportion obtained according to the analysis of the related data of each user.
In the embodiment of the invention, the rights and interests data sets in the historical sales data set are extracted and classified to obtain a plurality of rights and interests data sets, rights and interests data in the historical sales data set are deeply mined, rights and interests matrix corresponding to the rights and interests data sets are calculated respectively, the rights and interests matrix is summarized to obtain a feature set, feature selection processing is carried out on the feature set to obtain a plurality of feature subsets, diversity and richness of the feature subsets are guaranteed, a hyperplane function is constructed by the plurality of feature subsets, the feature subsets are classified by the hyperplane function, classification accuracy is improved, feature subsets consistent with the classification labels are screened out by comparing the classification results with preset classification labels, the classification labels are used as reference standards, screening accuracy is guaranteed, target rights and interests are selected from the rights and interests data subsets according to the feature subsets consistent with the classification labels, the target rights and interests data subsets are the benefits most suitable for users, and the target rights and interests are distributed to the plurality of users according to preset distribution rules. Therefore, the benefit distribution method based on feature selection can solve the problem of lower accuracy of the benefit distribution.
FIG. 2 is a functional block diagram of a rights and interests distribution apparatus based on feature selection according to an embodiment of the present invention.
The rights and interests distribution apparatus 100 based on feature selection according to the present invention may be installed in an electronic device. Depending on the functions implemented, the feature selection-based equity distribution apparatus 100 may include a data classification module 101, a matrix calculation module 102, a feature selection module 103, a subset classification module 104, a data screening module 105, and an equity distribution module 106. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data classification module 101 is configured to obtain a historical sales data set, extract a rights and interests data set in the historical sales data set, and classify the rights and interests data set to obtain a plurality of rights and interests data subsets;
the matrix calculation module 102 is configured to calculate the rights matrices corresponding to the rights data subsets, and aggregate the rights matrices to obtain a feature set;
The feature selection module 103 is configured to perform feature selection processing on the feature set to obtain a plurality of feature subsets;
the subset classification module 104 is configured to construct a hyperplane function according to the feature subsets, and classify the feature subsets by using the hyperplane function to obtain a classification result;
the data screening module 105 is configured to screen a feature subset consistent with a preset classification label by comparing the classification result with the classification label;
the rights allocation module 106 is configured to select a target rights from the rights data subset according to a feature subset consistent with the classification tag, and allocate the target rights to a plurality of users according to a preset allocation rule.
In detail, the specific embodiments of the modules of the rights distribution device 100 based on feature selection are as follows:
step one, acquiring a historical sales data set, extracting a rights and interests data set in the historical sales data set, and classifying the rights and interests data set to obtain a plurality of rights and interests data subsets.
In the embodiment of the invention, the historical sales data set is the historical service and marketing records of all clients in the historical service and marketing data of the insurance company.
Specifically, the extracting the rights data set in the historical sales data set and classifying the rights data set to obtain a plurality of rights data subsets includes:
acquiring a preset rights and interests classification table, wherein the rights and interests classification table comprises a plurality of rights and interests types and rights and interests data corresponding to the rights and interests types;
and distributing the historical sales data set into the plurality of rights categories according to the rights classification table to obtain a plurality of rights data subsets.
In detail, the rights classification table may include a plurality of rights categories and rights data corresponding to the plurality of rights categories.
For example, the plurality of rights categories include car washing rights, maintenance rights, proxy service rights, proxy driving service rights, detection adjustment rights, paint spraying rights, and sharing of basic knowledge science popularization links of car insurance. The rights data corresponding to the rights categories comprise relevant data such as car washing coupons issued to car insurance clients corresponding to car washing rights. And distributing the historical sales data set to the plurality of rights categories according to the rights classification table to obtain a plurality of rights data subsets, wherein the rights classification table comprises car washing rights, car washing coupons and other related data related to the car washing rights can be classified into one right of the car washing rights, and meanwhile, the car washing coupons and other related data related to the car washing rights are one of the rights data subsets.
And step two, respectively calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain a feature set.
In the embodiment of the present invention, the calculating the rights matrix corresponding to the plurality of rights data subsets, and summarizing the rights matrix to obtain the feature set includes:
analyzing pre-issuance data and actual usage data in the rights data subset;
substituting the pre-issued data and the actual use data into a preset rights formula for calculation to obtain a rights matrix corresponding to the rights data set;
and calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain the feature set.
In detail, the pre-distribution data refers to the distribution times of the equity of each category j in the equity data subset for each specific client i, and the sum of the actual use times under the actual use data line and the actual click times of the on-line link.
Specifically, the substituting the pre-issued data and the actual use data into a preset rights formula for calculation to obtain a rights matrix corresponding to the rights data set includes:
the preset equity formula is as follows:
Wherein x is ij To be a rights matrix, release ij For the pre-issue data, use ij For the actual usage data.
Further, each right data subset can be calculated to obtain a corresponding right matrix, and the right matrices corresponding to each right data subset are summarized to obtain a feature set.
And thirdly, performing feature selection processing on the feature set to obtain a plurality of feature subsets.
In the embodiment of the present invention, the improved LVW feature selection algorithm may be used for feature selection, where the improved LVW feature selection algorithm differs from the conventional LVW feature selection algorithm in that the conventional LVW feature selection algorithm may completely randomly select a feature subset, and a range may be preset in the scheme, and the feature subset may be selected and combined in the range.
Specifically, the feature selection processing is performed on the feature set to obtain a plurality of feature subsets, including:
screening a primary selected feature set meeting a preset range from the feature set;
and randomly arranging and combining the initially selected feature sets to obtain a plurality of feature subsets.
In detail, the preset range is beta epsilon [1, alpha ], the feature subset with 0 < alpha < omega dimension, wherein alpha is a preset value, and omega is the total dimension of the sample. And screening a primary selected feature set conforming to the preset range from the feature set, and randomly arranging and combining the primary selected features in the primary selected feature set to obtain a plurality of feature subsets.
For example, the feature set includes a feature a, a feature B, a feature C, a feature D, and a feature E, the initially selected feature set conforming to the preset range may be a feature a, a feature C, and a feature E, and the initially selected features in the initially selected feature set may be randomly arranged and combined, so that a plurality of feature subsets may be obtained, where the plurality of feature subsets are { feature a }, { feature C }, { feature E }, { feature a, feature C }, { feature a, feature E }, { feature C, feature E }, { feature a, feature C, and feature E }, respectively.
And fourthly, constructing a hyperplane function according to the feature subsets, and classifying the feature subsets by utilizing the hyperplane function to obtain a classification result.
In the embodiment of the present invention, the constructing a hyperplane function according to the feature subsets includes:
acquiring a preset label set, and taking the number of the feature subsets as feature dimensions;
constructing a multidimensional coordinate system consistent with the characteristic dimension according to the tag set and the characteristic dimension;
mapping the feature subsets into the multi-dimensional coordinate system to obtain feature coordinate sets;
calculating Euclidean distance between any two feature coordinates in the feature coordinate set, and selecting two feature coordinates with the minimum Euclidean distance as target feature coordinates;
And respectively taking the target feature coordinates as a left boundary and a right boundary, and constructing a hyperplane function in the middle of the left boundary and the right boundary.
In detail, in this solution, the tag set is a history tag of whether each client is actually renewing, for example, the category tag when being actually renewing is 1, and the category tag when not being actually renewing is 0. And if two feature subsets exist, the feature dimension is 2, the tag set is taken as the y axis, the feature subset is taken as the x axis to construct a two-dimensional coordinate system, and the feature subsets are mapped onto the two-dimensional coordinate system to obtain the feature coordinate set on the two-dimensional coordinate system. The target feature coordinates are respectively taken as a left boundary and a right boundary, the function of the left boundary can be w x+b=1, and the function of the right boundary can be w x+b= -1, so that the hyperplane function is w x+b=0.
Specifically, the classifying the feature subset by using the hyperplane function to obtain a classification result includes:
calculating a distance value from the hyperplane function to the target feature coordinates, and constructing a minimum distance function according to the distance value;
Constructing constraint conditions, wherein the constraint conditions are that the distance from each coordinate to the hyperplane is greater than or equal to a minimum distance function;
solving a minimum distance function based on the constraint condition by using a preset Lagrangian function to obtain a hyperplane;
and classifying the feature subsets according to the hyperplane to obtain a classification result.
Further, the calculating a distance value from the hyperplane function to the target feature coordinate includes:
calculating the distance value from the hyperplane function to the target feature coordinate according to a preset distance formula:
wherein, gamma i Is the distance value, x i For the ith target feature coordinate, y i And for the ith tag in the tag set, w and b are preset fixed parameters.
Specifically, the constructing a minimum distance function according to the distance value includes:
wherein gamma is a minimum distance function, gamma i Is a distance value.
In detail, a constraint condition is constructed, wherein the constraint condition is that the distance between each coordinate and the hyperplane is greater than or equal to a minimum distance function, and the constraint condition can be expressed as
Further, solving the minimum distance function based on the constraint condition by using a preset Lagrange function to obtain a hyperplane, wherein the method comprises the following steps:
Constructing the constraint condition and the minimum distance function into a Lagrangian objective function according to the Lagrangian function;
and solving the Lagrangian objective function to obtain a hyperplane.
In detail, the lagrangian objective function is:
wherein alpha is i For Lagrangian multiplier, w and b are preset fixed parameters, x i For the ith target feature coordinate, y i Is a label.
Specifically, the classification is performed on the feature subset according to the hyperplane, so as to obtain a classification result, for example, the hyperplane takes the feature subset { feature a }, { feature C } and { feature E } as a class, and the feature subset { feature a, feature C }, { feature a, feature E }, { feature C, feature E } and { feature a, feature C, feature E } as a class.
And fifthly, comparing the classification result with a preset classification label, and screening a feature subset consistent with the classification label.
In the embodiment of the invention, the preset classification labels are labels with a plurality of rights and interests with maximum continuous preservation possibility, which are set in advance, and the feature subsets consistent with the classification labels are screened out by comparing the classification results with the preset classification labels.
In detail, the accuracy of the feature subset finally screened out can be ensured by comparing and screening.
And step six, selecting target interests from the interest data subsets according to the feature subsets consistent with the classification labels, and distributing the target interests to a plurality of users according to a preset distribution rule.
In the embodiment of the invention, the feature subset consistent with the classification label is used as a reference, the target equity is selected from the equity data subset, and the target equity is distributed to a plurality of users according to a preset distribution rule, wherein the distribution rule can be distribution proportion of the target equity according to market research analysis.
For example, when the selected target equity is one, the target equity may be a maintenance equity, multiple users need to be screened and the maintenance equity is allocated to users meeting the screening requirement among the multiple users, and when the obtained target equity is multiple, the multiple target equity may be a paint spray equity, a car washing equity and a proxy service equity, and the target equity is allocated to the multiple users according to the allocation proportion obtained by the market research analysis according to the preset allocation rule. The distribution proportion obtained according to the market research analysis is the proportion obtained according to the analysis of the related data of each user.
In the embodiment of the invention, the rights and interests data sets in the historical sales data set are extracted and classified to obtain a plurality of rights and interests data sets, rights and interests data in the historical sales data set are deeply mined, rights and interests matrix corresponding to the rights and interests data sets are calculated respectively, the rights and interests matrix is summarized to obtain a feature set, feature selection processing is carried out on the feature set to obtain a plurality of feature subsets, diversity and richness of the feature subsets are guaranteed, a hyperplane function is constructed by the plurality of feature subsets, the feature subsets are classified by the hyperplane function, classification accuracy is improved, feature subsets consistent with the classification labels are screened out by comparing the classification results with preset classification labels, the classification labels are used as reference standards, screening accuracy is guaranteed, target rights and interests are selected from the rights and interests data subsets according to the feature subsets consistent with the classification labels, the target rights and interests data subsets are the benefits most suitable for users, and the target rights and interests are distributed to the plurality of users according to preset distribution rules. Therefore, the benefit distribution device based on feature selection can solve the problem of lower accuracy of the benefit distribution.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a rights and interests allocation method based on feature selection according to an embodiment of the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication interface 12 and a bus 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a benefit distribution program selected based on features.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of a rights allocation program based on feature selection, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes programs or modules (e.g., a benefit distribution program based on feature selection, etc.) stored in the memory 11 by running or executing the programs or modules, and invokes data stored in the memory 11 to perform various functions of the electronic device and process the data.
The communication interface 12 is used for communication between the electronic device and other devices, including network interfaces and user interfaces. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
The bus 13 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus 13 may be classified into an address bus, a data bus, a control bus, and the like. The bus 13 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Further, the electronic device may also include a network interface, optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the electronic device may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The feature selection based benefit distribution program stored by the memory 11 in the electronic device is a combination of instructions that, when executed in the processor 10, may implement:
Acquiring a historical sales data set, extracting a rights and interests data set in the historical sales data set, and classifying the rights and interests data set to obtain a plurality of rights and interests data subsets;
respectively calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain a feature set;
performing feature selection processing on the feature set to obtain a plurality of feature subsets;
constructing a hyperplane function according to the feature subsets, and classifying the feature subsets by utilizing the hyperplane function to obtain a classification result;
screening a feature subset consistent with the classification label by comparing the classification result with a preset classification label;
and selecting the target interests from the interest data subsets according to the feature subsets consistent with the classification labels, and distributing the target interests to a plurality of users according to preset distribution rules.
Specifically, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring a historical sales data set, extracting a rights and interests data set in the historical sales data set, and classifying the rights and interests data set to obtain a plurality of rights and interests data subsets;
respectively calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain a feature set;
performing feature selection processing on the feature set to obtain a plurality of feature subsets;
constructing a hyperplane function according to the feature subsets, and classifying the feature subsets by utilizing the hyperplane function to obtain a classification result;
screening a feature subset consistent with the classification label by comparing the classification result with a preset classification label;
and selecting the target interests from the interest data subsets according to the feature subsets consistent with the classification labels, and distributing the target interests to a plurality of users according to preset distribution rules.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A method of feature selection-based rights allocation, the method comprising:
acquiring a historical sales data set, extracting a rights and interests data set in the historical sales data set, and classifying the rights and interests data set to obtain a plurality of rights and interests data subsets;
respectively calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain a feature set;
performing feature selection processing on the feature set to obtain a plurality of feature subsets;
Constructing a hyperplane function according to the feature subsets, and classifying the feature subsets by utilizing the hyperplane function to obtain a classification result;
screening a feature subset consistent with the classification label by comparing the classification result with a preset classification label;
selecting a target interest from the interest data subset according to the feature subset consistent with the classification tag, and distributing the target interest to a plurality of users according to a preset distribution rule;
wherein the constructing a hyperplane function according to the feature subsets includes: acquiring a preset label set, and taking the number of the feature subsets as feature dimensions; constructing a multidimensional coordinate system consistent with the characteristic dimension according to the tag set and the characteristic dimension; mapping the feature subsets into the multi-dimensional coordinate system to obtain feature coordinate sets; calculating Euclidean distance between any two feature coordinates in the feature coordinate set, and selecting two feature coordinates with the smallest Euclidean distance as target feature coordinates; respectively taking the target feature coordinates as a left boundary and a right boundary, and constructing a hyperplane function in the middle of the left boundary and the right boundary;
The classifying the feature subset by using the hyperplane function to obtain a classification result comprises: calculating a distance value from the hyperplane function to the target feature coordinates, and constructing a minimum distance function according to the distance value; constructing constraint conditions, wherein the constraint conditions are that the distance from each coordinate to the hyperplane is greater than or equal to a minimum distance function; solving a minimum distance function based on the constraint condition by using a preset Lagrangian function to obtain a hyperplane; classifying the feature subsets according to the hyper-planes to obtain classification results;
the calculating a distance value from the hyperplane function to the target feature coordinate includes: calculating the distance value from the hyperplane function to the target feature coordinate according to a preset distance formula:
wherein, gamma i Is the distance value, x i For the ith target feature coordinate, y i And for the ith tag in the tag set, w and b are preset fixed parameters.
2. The feature selection-based rights allocation method of claim 1, wherein performing feature selection processing on the feature set to obtain a plurality of feature subsets comprises:
screening a primary selected feature set meeting a preset range from the feature set;
And randomly arranging and combining the initially selected feature sets to obtain a plurality of feature subsets.
3. The feature selection-based equity distribution method of claim 1 wherein said extracting equity data sets in said historical sales data sets and classifying said equity data sets to obtain a plurality of equity data subsets comprises:
acquiring a preset rights and interests classification table, wherein the rights and interests classification table comprises a plurality of rights and interests types and rights and interests data corresponding to the rights and interests types;
and distributing the historical sales data set into the plurality of rights categories according to the rights classification table to obtain a plurality of rights data subsets.
4. The feature selection-based rights allocation method of claim 1, wherein calculating the rights matrices corresponding to the plurality of rights data subsets, respectively, and summarizing the rights matrices to obtain feature sets, comprises:
analyzing pre-issuance data and actual usage data in the rights data subset;
substituting the pre-issued data and the actual use data into a preset rights formula for calculation to obtain a rights matrix corresponding to the rights data set;
and calculating the right matrixes corresponding to the right data subsets, and summarizing the right matrixes to obtain the feature set.
5. A feature selection-based rights allocation apparatus for implementing the feature selection-based rights allocation method according to any one of claims 1 to 4, the apparatus comprising:
the data classification module is used for acquiring a historical sales data set, extracting a rights and interests data set in the historical sales data set and classifying the rights and interests data set to obtain a plurality of rights and interests data subsets;
the matrix calculation module is used for calculating the right matrixes corresponding to the right data subsets respectively and summarizing the right matrixes to obtain a feature set;
the feature selection module is used for carrying out feature selection processing on the feature set to obtain a plurality of feature subsets;
the subset classification module is used for constructing a hyperplane function according to the feature subsets, classifying the feature subsets by utilizing the hyperplane function and obtaining a classification result;
the data screening module is used for screening a feature subset consistent with the classification label by comparing the classification result with a preset classification label;
and the rights and interests distribution module is used for selecting target rights and interests from the rights and interests data subsets according to the feature subsets consistent with the classification labels and distributing the target rights and interests to a plurality of users according to a preset distribution rule.
6. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the feature selection-based benefit distribution method of any one of claims 1 to 4.
7. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the feature selection based equity distribution method according to any of claims 1 to 4.
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