CN106326740A - Evaluation method and device of data feature selection - Google Patents
Evaluation method and device of data feature selection Download PDFInfo
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Abstract
The invention discloses an evaluation method and device of data feature selection, relates to the technical field of data mining and aims to solve the problem of failing to ensure if feature selection is correct and optimal when conducting network safety information data mining. The method comprises the steps of obtaining a data matrix of the feature selection to be evaluated; conducting sorting of the data matrix according to different types of attributive characters; configuring weight coefficient corresponding to the types for numerical value in the data matrix; configuring evaluation value of the data matrix after configuring weight coefficient through preset product algorithm and preset sum algorithm. The evaluation method and device of data feature selection is mainly applied to evaluation of data feature selection.
Description
Technical field
The present invention relates to a kind of data mining technology field, particularly relate to evaluation methodology that a kind of data characteristics selects and
Device.
Background technology
The network information security refers to that the data in the hardware of network system, software and system thereof can be protected, and is not subject to
Accidental or the reason of malice and suffering is destroyed, changes, is revealed so that network can system continuously, reliably, normally
Run.In order to analyze the network information whether safety, by the big data produced in network are carried out data mining, thus can carry
Take out the data message of user's request, but, feature selection is the pre-treatment step that data mining is important.
At present, existing feature selection is to choose a small amount of attribute from higher-dimension attribute matrix, defeated as data mining
Enter attribute, but, the feature of calculated data is processed directly as the input of data mining, it is impossible to guarantee feature
Choose whether correct and optimum, thus cause the inefficient of data mining.
Summary of the invention
In view of this, the present invention provides the evaluation methodology and device that a kind of data characteristics selects, and main purpose is to solve
When carrying out network safety information data mining, it is impossible to guarantee the problem that feature selection is the most correct and optimum.
According to one aspect of the invention, it is provided that the evaluation methodology that a kind of data characteristics selects, including:
Obtain the data matrix of feature selection to be evaluated；
According to different attribute characteristic type, described data matrix is classified；
The weights coefficient corresponding with described type is configured for the numerical value in data matrix；
By preset product algorithm and preset summation algorithm, calculate the evaluation of estimate of the data matrix after configuration weights coefficient.
According to one aspect of the invention, it is provided that the evaluating apparatus that a kind of data characteristics selects, including:
Acquiring unit, for obtaining the data matrix of feature selection to be evaluated；
Taxon, for classifying to described data matrix according to different attribute characteristic type；
Dispensing unit, for configuring the weights coefficient corresponding with described type for the numerical value in data matrix；
Computing unit, for by preset product algorithm and preset summation algorithm, calculating the data after configuration weights coefficient
The evaluation of estimate of matrix.
By technique scheme, the technical scheme that the embodiment of the present invention provides at least has the advantage that
The present invention executes evaluation methodology and the device that example provides a kind of data characteristics to select, and first obtains feature selection to be evaluated
Data matrix, then according to different attribute characteristic type, described data matrix is classified, then is the number in data matrix
The weights coefficient that value configuration is corresponding with described type, finally by preset product algorithm and preset summation algorithm, calculates configuration power
The evaluation of estimate of the data matrix after value coefficient.The embodiment of the present invention is carried out after data process by feature selection obtains data
Evaluation of estimate to feature selection, it is achieved the evaluation to feature selection result, it is simple to feature selection result is handled it, thus carries
The evaluation efficiency that high data characteristics selects.
Described above is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention,
And can be practiced according to the content of description, and in order to allow above and other objects of the present invention, the feature and advantage can
Become apparent, below especially exemplified by the detailed description of the invention of the present invention.
Accompanying drawing explanation
By reading the detailed description of hereafter preferred implementation, various other advantage and benefit common for this area
Technical staff will be clear from understanding.Accompanying drawing is only used for illustrating the purpose of preferred implementation, and is not considered as the present invention
Restriction.And in whole accompanying drawing, it is denoted by the same reference numerals identical parts.In the accompanying drawings:
Fig. 1 shows the schematic diagram of the evaluation methodology that a kind of data characteristics that the embodiment of the present invention one provides selects；
Fig. 2 shows the schematic diagram of the evaluation methodology that the another kind of data characteristics that the embodiment of the present invention two provides selects；
Fig. 3 shows the structural representation of the evaluating apparatus that a kind of data characteristics that the embodiment of the present invention three provides selects；
Fig. 4 shows the structural representation of the evaluating apparatus that a kind of data characteristics that the embodiment of the present invention four provides selects.
Detailed description of the invention
It is more fully described the exemplary embodiment of the disclosure below with reference to accompanying drawings.Although accompanying drawing shows the disclosure
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure and should be by embodiments set forth here
Limited.On the contrary, it is provided that these embodiments are able to be best understood from the disclosure, and can be by the scope of the present disclosure
Complete conveys to those skilled in the art.
The embodiment of the present invention provides the evaluation methodology that a kind of data characteristics selects, as it is shown in figure 1, described method includes:
101, the data matrix of feature selection to be evaluated is obtained.
Wherein, described data matrix is the data matrix comprising attribute character, and the line number of each data matrix and columns
The most identical.
It should be noted that data matrix can be stored in a preset position, when needs calculate, difference is utilized to hold
The program of the software that row calculates gets multiple data matrix.
Such as, when using matlab software to calculate, if data are stored in excel file, then put down at matlab
In platform, the program of excel file is called in input, obtains matrix a.
102, according to different attribute characteristic type, described data matrix is classified.
Wherein, described different attribute characteristic type can include all data attribute types in network safety information, this
Inventive embodiments is not specifically limited.
Such as, attribute character type includes security type and hazard types, obtains security type and comprise matrix a after classification,
Or hazard types includes matrix b.
103, the weights coefficient corresponding with described type is configured for the numerical value in data matrix.
Wherein, the weights coefficient that weights coefficient is dissimilar matrix of described correspondence is different, and described weights coefficient is permissible
Permutation and combination for different ordered series of numbers, it is also possible to for different probability distribution, it is also possible to for the best initial weights utilizing model training to go out,
The embodiment of the present invention is not specifically limited.
Such as, the matrix A of security type, the weights after the weights coefficient of numerical value configuration can be RL model training in matrix
Coefficient.
104, by preset product algorithm and preset summation algorithm, the evaluation of the data matrix after configuration weights coefficient is calculated
Value.
Wherein, institute's evaluation values is a numerical value, generally on the occasion of, numerical value is the biggest, and the efficiency that representative feature selects is the best,
Evaluation of estimate is for analyzing the quality of feature selection, in order to user carries out further data mining.
The present invention executes the evaluation methodology that example provides a kind of data characteristics to select, and first obtains the data of feature selection to be evaluated
Matrix, then classifies to described data matrix according to different attribute characteristic type, then is the numerical value configuration in data matrix
The weights coefficient corresponding with described type, finally by preset product algorithm and preset summation algorithm, calculates configuration weights coefficient
After the evaluation of estimate of data matrix.The embodiment of the present invention is by obtaining feature after feature selection obtains data carrying out data process
The evaluation of estimate selected, it is achieved the evaluation to feature selection result, it is simple to feature selection result is handled it, thus improves data
The evaluation efficiency of feature selection.
The embodiment of the present invention provides the evaluation methodology that another kind of data characteristics selects, as in figure 2 it is shown, described method includes:
201, the data matrix of feature selection to be evaluated is configured to the data matrix that ranks quantity is identical.
Wherein, described ranks quantity is all the row of same number and the row of same number mutually, by feature to be evaluated being selected
The data matrix selected is configured to the data matrix that ranks quantity is identical, in order to carries out the product of row and column in matrix, thus improves
The evaluation efficiency that data characteristics selects.
202, the data matrix of feature selection to be evaluated is obtained.
This step is identical with the method described in step 101 described in Fig. 1, repeats no more here.
203, according to different attribute characteristic type, described data matrix is classified.
Wherein, described attribute character type includes user type, security type, hazard types.
If 204a attribute character type is user type, then be in described data matrix numerical value configuration weights coefficient
Meet binomial distribution.
Wherein, described binomial distribution is the binomial distribution on probability meaning, and specially 0-1 is distributed, and described user type is
The attribute that user was labeled, can include the demand properties of user, and the embodiment of the present invention is not specifically limited.Such as, for
User type, then be 1*a1 by the data configuration in matrix, 0*a2,1*a3 etc..
For the embodiment of the present invention, if the step 204b attribute character type arranged side by side with step 204a is security type, then
The weights coefficient configured for the numerical value in described data matrix meets normal distribution.
Wherein, described normal distribution is the normal distribution on probability meaning, and concrete numerical value can choose numerical value in matrix
The maximum maximum in the most too distribution, successively decreases to both sides with this.
For the embodiment of the present invention, if the step 204c attribute character type arranged side by side with step 204a is hazard types, then
The weights coefficient index of coincidence distribution configured for the numerical value in described data matrix.
Wherein, described exponential is the exponential on probability meaning, when the numerical value in hazard types matrix is the most greatly
Illustrating that danger coefficient is the biggest, the index weights of configuration are the biggest.
205, it is calculated multiple result of calculation by the product algorithm of preset row and column.
Wherein, the product algorithm of described preset row and column is the dot-product operation of matrix in linear algebra, such as, matrix
{ a11, a12, a13；A21, a22, a23；In a31, a32, a33}, behavior a1={a11, a12, a13}, a2={a21, a22,
A23}, a3{a31, a32, a33}, be classified as b1={a11, a21, a31}, b2={a12, a22, a32}, b3={a13, a23,
A33}, product algorithm is c1=a1*b1, c2=a2*b2, c3=a3*b3.
206, the plurality of result of calculation is carried out summation statistics and obtains evaluation of estimate.
Such as, above-mentioned calculated result c1, c2, c3 are added, obtain evaluation of estimate D.
Further, the embodiment of the present invention can also include: calculates according to different user's requests and different data minings
Method judges whether to data mining；If desired, then warning information is sent.Wherein, described different user's request includes data
The different application scene excavated, e.g., the big data process etc. in the big data process of the network information security, commercial production, this
Bright is that embodiment is not specifically limited.Described data mining algorithm includes the group of different types of rote learning or algorithms of different
Closing, the embodiment of the present invention is not specifically limited.The described warning information that sends includes sound alarm and image alarm, and the present invention implements
Example is not specifically limited.Dig by judging whether to data according to different user's requests and different data mining algorithms
Pick, if desired, then send warning information, it is achieved evaluate the optimal feature selection value of applicable current scene under different scenes,
To improve the efficiency of data mining.
The present invention executes the evaluation methodology that example provides another kind of data characteristics to select, and first obtains the number of feature selection to be evaluated
According to matrix, then according to different attribute characteristic type, described data matrix is classified, then join for the numerical value in data matrix
Put the weights coefficient corresponding with described type, finally by preset product algorithm and preset summation algorithm, calculate configuration weights system
The evaluation of estimate of the data matrix after number.The embodiment of the present invention is by obtaining spy after feature selection obtains data carrying out data process
Levy the evaluation of estimate of selection, it is achieved the evaluation to feature selection result, it is simple to feature selection result is handled it, thus improves number
Evaluation efficiency according to feature selection.
The embodiment of the present invention provides the evaluating apparatus that a kind of data characteristics selects, as it is shown on figure 3, described method includes: obtain
Take unit 31, taxon 32, dispensing unit 33, computing unit 34.
Acquiring unit 31, for obtaining the data matrix of feature selection to be evaluated；
Taxon 32, for classifying to described data matrix according to different attribute characteristic type；
Dispensing unit 33, for configuring the weights coefficient corresponding with described type for the numerical value in data matrix；
Computing unit 34, for by preset product algorithm and preset summation algorithm, calculating the number after configuration weights coefficient
Evaluation of estimate according to matrix.
The present invention executes the evaluating apparatus that example provides a kind of data characteristics to select, and first obtains the data of feature selection to be evaluated
Matrix, then classifies to described data matrix according to different attribute characteristic type, then is the numerical value configuration in data matrix
The weights coefficient corresponding with described type, finally by preset product algorithm and preset summation algorithm, calculates configuration weights coefficient
After the evaluation of estimate of data matrix.The embodiment of the present invention is by obtaining feature after feature selection obtains data carrying out data process
The evaluation of estimate selected, it is achieved the evaluation to feature selection result, it is simple to feature selection result is handled it, thus improves data
The evaluation efficiency of feature selection.
The embodiment of the present invention provides the evaluating apparatus that another kind of data characteristics selects, and as shown in Figure 4, described method includes:
Acquiring unit 41, taxon 42, dispensing unit 43, computing unit 44, judging unit 45, arithmetic element 46.
Acquiring unit 41, for obtaining the data matrix of feature selection to be evaluated；
Taxon 42, for classifying to described data matrix according to different attribute characteristic type；
Dispensing unit 43, for configuring the weights coefficient corresponding with described type for the numerical value in data matrix；
Computing unit 44, for by preset product algorithm and preset summation algorithm, calculating the number after configuration weights coefficient
Evaluation of estimate according to matrix.
Described dispensing unit 43, is additionally operable to the data matrix of feature selection to be evaluated is configured to the number that ranks quantity is identical
According to matrix.
Described dispensing unit 43, if being user type specifically for attribute character type, is then in described data matrix
The weights coefficient of numerical value configuration meets binomial distribution；
Described dispensing unit 43, if being specifically additionally operable to attribute character type is security type, is then in described data matrix
Numerical value configuration weights coefficient meet normal distribution；
Described dispensing unit 43, if being specifically additionally operable to attribute character type is hazard types, is then in described data matrix
Numerical value configuration weights coefficient index of coincidence distribution.
Further, described computing unit 44 includes:
Computing module 4401, for being calculated multiple result of calculation by the product algorithm of preset row and column；
Statistical module 4402, obtains evaluation of estimate for the plurality of result of calculation carries out summation statistics.
Further, described device also includes:
Judging unit 45, for judging whether to data according to different user's requests and different data mining algorithms
Excavate；
Arithmetic element 46, if if judging to calculate according to different user's requests and different data minings for judging unit
Method does not carry out data mining, then send warning information.
The present invention executes the evaluating apparatus that example provides another kind of data characteristics to select, and first obtains the number of feature selection to be evaluated
According to matrix, then according to different attribute characteristic type, described data matrix is classified, then join for the numerical value in data matrix
Put the weights coefficient corresponding with described type, finally by preset product algorithm and preset summation algorithm, calculate configuration weights system
The evaluation of estimate of the data matrix after number.The embodiment of the present invention is by obtaining spy after feature selection obtains data carrying out data process
Levy the evaluation of estimate of selection, it is achieved the evaluation to feature selection result, it is simple to feature selection result is handled it, thus improves number
Evaluation efficiency according to feature selection.
In the above-described embodiments, the description to each embodiment all emphasizes particularly on different fields, and does not has the portion described in detail in certain embodiment
Point, may refer to the associated description of other embodiments.
It is understood that the correlated characteristic in said method and device can mutually reference.It addition, in above-described embodiment
" first ", " second " etc. be for distinguishing each embodiment, and do not represent the quality of each embodiment.
Those skilled in the art is it can be understood that arrive, for convenience and simplicity of description, and the system of foregoing description,
The specific works process of device and unit, is referred to the corresponding process in preceding method embodiment, does not repeats them here.
Algorithm and display are not intrinsic to any certain computer, virtual system or miscellaneous equipment relevant provided herein.
Various general-purpose systems can also be used together with based on teaching in this.As described above, construct required by this kind of system
Structure be apparent from.Additionally, the present invention is also not for any certain programmed language.It is understood that, it is possible to use various
Programming language realizes the content of invention described herein, and the description done language-specific above is to disclose this
Bright preferred forms.
In description mentioned herein, illustrate a large amount of detail.It is to be appreciated, however, that the enforcement of the present invention
Example can be put into practice in the case of not having these details.In some instances, it is not shown specifically known method, structure
And technology, in order to do not obscure the understanding of this description.
Similarly, it will be appreciated that one or more in order to simplify that the disclosure helping understands in each inventive aspect, exist
Above in the description of the exemplary embodiment of the present invention, each feature of the present invention is grouped together into single enforcement sometimes
In example, figure or descriptions thereof.But, the method for the disclosure should not be construed to reflect an intention that i.e. required guarantor
The application claims feature more more than the feature being expressly recited in each claim protected.More precisely, as following
Claims reflected as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
The claims following detailed description of the invention are thus expressly incorporated in this detailed description of the invention, the most each claim itself
All as the independent embodiment of the present invention.
Those skilled in the art are appreciated that and can carry out the module in the equipment in embodiment adaptively
Change and they are arranged in one or more equipment different from this embodiment.Can be the module in embodiment or list
Unit or assembly are combined into a module or unit or assembly, and can put them in addition multiple submodule or subelement or
Sub-component.In addition at least some in such feature and/or process or unit excludes each other, can use any
Combine all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so disclosed appoint
Where method or all processes of equipment or unit are combined.Unless expressly stated otherwise, this specification (includes adjoint power
Profit requires, summary and accompanying drawing) disclosed in each feature can be carried out generation by providing identical, equivalent or the alternative features of similar purpose
Replace.
Although additionally, it will be appreciated by those of skill in the art that embodiments more described herein include other embodiments
Some feature included by rather than further feature, but the combination of the feature of different embodiment means to be in the present invention's
Within the scope of and form different embodiments.Such as, in the following claims, embodiment required for protection appoint
One of meaning can mode use in any combination.
The all parts embodiment of the present invention can realize with hardware, or to run on one or more processor
Software module realize, or with combinations thereof realize.It will be understood by those of skill in the art that and can use in practice
Microprocessor or digital signal processor (DSP) realize the evaluation methodology that data characteristics according to embodiments of the present invention selects
And the some or all functions of the some or all parts in device.The present invention is also implemented as performing institute here
Part or all equipment of the method described or device program (such as, computer program and computer program product
Product).The program of such present invention of realization can store on a computer-readable medium, or can have one or more
The form of signal.Such signal can be downloaded from internet website and obtain, or provides on carrier signal, or to appoint
What his form provides.
The present invention will be described rather than limits the invention to it should be noted above-described embodiment, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference marks that should not will be located between bracket is configured to limitations on claims.Word " comprises " and does not excludes the presence of not
Arrange element in the claims or step.Word "a" or "an" before being positioned at element does not excludes the presence of multiple such
Element.The present invention and can come real by means of including the hardware of some different elements by means of properly programmed computer
Existing.If in the unit claim listing equipment for drying, several in these devices can be by same hardware branch
Specifically embody.Word first, second and third use do not indicate that any order.These word explanations can be run after fame
Claim.
Claims (10)
1. the evaluation methodology that a data characteristics selects, it is characterised in that including:
Obtain the data matrix of feature selection to be evaluated；
According to different attribute characteristic type, described data matrix is classified；
The weights coefficient corresponding with described type is configured for the numerical value in data matrix；
By preset product algorithm and preset summation algorithm, calculate the evaluation of estimate of the data matrix after configuration weights coefficient.
The evaluation methodology that data characteristics the most according to claim 1 selects, it is characterised in that described acquisition feature to be evaluated
Before the data matrix selected, described method includes:
The data matrix of feature selection to be evaluated is configured to the data matrix that ranks quantity is identical.
The evaluation methodology that data characteristics the most according to claim 2 selects, it is characterised in that described attribute character type bag
Include user type, security type, hazard types, the described weights system corresponding with described type for the numerical value configuration in data matrix
Number includes:
If attribute character type is user type, then the weights coefficient configured for the numerical value in described data matrix meets binomial and divides
Cloth；
If attribute character type is security type, then the weights coefficient configured for the numerical value in described data matrix meets normal state and divides
Cloth；
If attribute character type is hazard types, then the weights coefficient index of coincidence for the numerical value configuration in described data matrix divides
Cloth.
The evaluation methodology that data characteristics the most according to claim 3 selects, it is characterised in that described calculated by preset product
Method and preset summation algorithm, the evaluation of estimate calculating the data matrix after configuration weights coefficient includes:
It is calculated multiple result of calculation by the product algorithm of preset row and column；
The plurality of result of calculation is carried out summation statistics and obtains evaluation of estimate.
The evaluation methodology that data characteristics the most according to claim 4 selects, it is characterised in that described method also includes:
Data mining is judged whether to according to different user's requests and different data mining algorithms；
If it is not, then send warning information.
6. the evaluating apparatus that a data characteristics selects, it is characterised in that including:
Acquiring unit, for obtaining the data matrix of feature selection to be evaluated；
Taxon, for classifying to described data matrix according to different attribute characteristic type；
Dispensing unit, for configuring the weights coefficient corresponding with described type for the numerical value in data matrix；
Computing unit, for by preset product algorithm and preset summation algorithm, calculating the data matrix after configuration weights coefficient
Evaluation of estimate.
The evaluating apparatus that data characteristics the most according to claim 6 selects, it is characterised in that
Described dispensing unit, is additionally operable to be configured to the data matrix of feature selection to be evaluated the data square that ranks quantity is identical
Battle array.
The evaluating apparatus that data characteristics the most according to claim 7 selects, it is characterised in that
Described dispensing unit, if being user type specifically for attribute character type, then joins for the numerical value in described data matrix
The weights coefficient put meets binomial distribution；
Described dispensing unit, if being specifically additionally operable to attribute character type is security type, is then the numerical value in described data matrix
The weights coefficient of configuration meets normal distribution；
Described dispensing unit, if being specifically additionally operable to attribute character type is hazard types, is then the numerical value in described data matrix
The weights coefficient index of coincidence distribution of configuration.
The evaluating apparatus that data characteristics the most according to claim 8 selects, it is characterised in that described computing unit includes:
Computing module, for being calculated multiple result of calculation by the product algorithm of preset row and column；
Statistical module, obtains evaluation of estimate for the plurality of result of calculation carries out summation statistics.
The evaluating apparatus that data characteristics the most according to claim 9 selects, it is characterised in that described method also includes:
Judging unit, for judging whether to data mining according to different user's requests and different data mining algorithms；
Arithmetic element, if if judging not enter according to different user's requests and different data mining algorithms for judging unit
Row data mining, then send warning information.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711194A (en) * | 2018-12-25 | 2019-05-03 | 北京天融信网络安全技术有限公司 | A kind of data processing method and data processing equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711194A (en) * | 2018-12-25 | 2019-05-03 | 北京天融信网络安全技术有限公司 | A kind of data processing method and data processing equipment |
| CN109711194B (en) * | 2018-12-25 | 2020-12-11 | 北京天融信网络安全技术有限公司 | Data processing method and data processing device |
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Application publication date: 20170111 |