CN110490234A - The construction method and classification method of classifier based on Cluster Classification associative mechanism - Google Patents

The construction method and classification method of classifier based on Cluster Classification associative mechanism Download PDF

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CN110490234A
CN110490234A CN201910652753.3A CN201910652753A CN110490234A CN 110490234 A CN110490234 A CN 110490234A CN 201910652753 A CN201910652753 A CN 201910652753A CN 110490234 A CN110490234 A CN 110490234A
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何发智
李浩然
周伟清
陈壹林
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Wuhan University WHU
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Abstract

The invention discloses the construction method and classification method of a kind of classifier based on Cluster Classification associative mechanism, classifier construction method therein includes: to obtain initialization population firstly, encoding the distribution situation of cluster centre into population at individual；Then optimal population is searched for using multi-objective optimization algorithm, as the Pareto disaggregation for meeting cluster two targets of objective function and class object function；Optimal solution is selected from Pareto solution concentration further according to mutual information index, and according to the optimal solution selected, the parameter of the classifier of joint Cluster Classification mechanism is configured, the classifier of the parameter building Cluster Classification associative mechanism based on setting.The present invention with method can be effectively by Cluster-Fusion into classification task, to improve accuracy and the search efficiency of classifier.

Description

The construction method and classification method of classifier based on Cluster Classification associative mechanism
Technical field
The present invention relates to data minings and big data technical field, and in particular to a kind of based on Cluster Classification associative mechanism The construction method and classification method of classifier.
Background technique
In data mining and big data field, cluster and classification are two mutually independent processes.Cluster is based on unsupervised Study can forecast sample label by the potential structure of mining data, be classified based on supervised learning, relationship square can be passed through Battle array predicts the label of unknown data.When classification information gives, the two can receive benefits mutually.Based on this, fuzzy relation Classifier proposes hereafter to improve the demand of aspect, parallel Cluster Classification calculation based on efficiency as serial Cluster Classification algorithm Method proposes to carry out classification task.
At least there is following technology in implementing the present invention, it may, the method for finding the prior art in present inventor Problem:
In the prior art, none multiple-objection optimization search strategy efficiently customized is applied to parallel clustering classification and calculates Inside method.Also, current cluster objective function can spend it is a large amount of calculate the time and meanwhile instruct effect be not it is especially outstanding, In addition, the frame of current parallel clustering sorting algorithm is not classic frame.
It follows that the bad technical problem of classifier performance exists in the prior art.
Summary of the invention
In view of this, the present invention provides a kind of construction method of classifier based on Cluster Classification associative mechanism and dividing Class method, to solve or at least partly solve the bad technical problem of performance of classifier existing in the prior art.
The present invention provides a kind of construction methods of classifier based on Cluster Classification associative mechanism, comprising:
Step S1: the distribution situation of cluster centre is encoded into population at individual, initialization population is obtained, wherein is initial Change includes cluster objective function and class object function in population；
Step S2: searching for optimal population using multi-objective optimization algorithm, clusters objective function as meeting and divides The Pareto disaggregation of two targets of class objective function；
Step S3: optimal solution is selected from Pareto solution concentration according to mutual information index, and optimal according to what is selected Solution, the parameter of classifier of joint Cluster Classification mechanism is configured, then the building Cluster Classification connection of the parameter based on setting The classifier of conjunction mechanism.
In one embodiment, step S1 is specifically included:
Step S1.1: multiple populations are directed to, corresponding cluster centre distribution situation is set；
Step S1.2: coding cluster centre distribution situation, and it will be used as decision variable, according to fitness calculation formula, meter Corresponding cluster target and class object value are calculated, and using corresponding cluster target as cluster objective function, class object value As class object function, wherein shown in the concrete form such as formula (1) and formula (2) of cluster objective function and class object function:
In formula (1), f (xi) label predicted of presentation class device, yiExpression is really label, δ (f (xi), yi) indicate pre- Whether mark label are consistent with true tag, consistent results 1, inconsistent results 0, and N indicates test data total quantity；
In formula (2), nnijIndicate distance sample xiMinimum distance j-th of sample, p (ck|nnij) indicate j-th of sample Belong to the conditional probability of k-th of cluster,It is a parameter, works as nnijAnd xiWhen belonging to cluster k, it is 0, is otherwise 1/j；
Step S1.3: decision variable, cluster objective function and class object function are encapsulated into population at individual simultaneously, Multiple individual composition initialization populations.
In one embodiment, step S2 is specifically included:
Step S2.1: it in former generation, is evolved using the crossover operator and mutation operator of difference algorithm to current population Processing, obtains progeny population；
Step S2.2: in former generation, using backward learning algorithm, inverse change is carried out to progeny population, and will be reversed The population of front and back carries out non-dominated ranking after merging, and obtains reversed sub- population；
Step S2.3: in former generation, using fireworks algorithm, calculating the burst radius and spark number of reversed sub- population, And it is based on burst radius and spark number, to generate spark population in peripheral extent using reversed sub- population as fireworks population, Fireworks population and spark population carry out non-dominated ranking and generate next-generation population；
Step S2.4: using next-generation population as former generation population is worked as, step S2.1~step S2.3 is repeated, is changed For evolutionary process；
Step S2.5: when iterative evolution process meets termination condition, iterative evolution is terminated, and exports Pareto disaggregation.
In one embodiment, the termination condition in step S2.5 includes: that the number of iterations satisfaction reaches greatest iteration time The whole variation of several and population is less than the minimum population variation of setting.
In one embodiment, step S3 is specifically included
Step S3.1: the association relationship of all Pareto disaggregation is calculated；
Step S3.2: according to the size of association relationship, the maximum Pareto disaggregation of association relationship is selected as optimal Solution is configured the parameter of the classifier of joint Cluster Classification mechanism；
Step S3.3: the classifier of the parameter building Cluster Classification associative mechanism based on setting.
In one embodiment, after step S3.3, the method also includes step S3.4:
It is tested using classifier of the test data to building.
Based on same inventive concept, second aspect of the present invention provides a kind of based on classifier constructed by first aspect Classification method, which includes:
After the cluster centre and clustering relationships that calculate data to be processed, classification results are obtained by classifier.This Shen Please said one or multiple technical solutions in embodiment, at least there are following one or more technical effects:
The construction method of a kind of classifier based on Cluster Classification associative mechanism provided by the invention, firstly, initialization kind Group, the distribution situation of cluster centre is encoded into population at individual, then searches for optimal population using multi-objective optimization algorithm, Make it as the Pareto disaggregation for meeting two targets, selects optimal solution from Pareto solution concentration further according to mutual information index The parameter setting of classifier as joint Cluster Classification mechanism.
Due to the construction method of the classifier of Cluster Classification associative mechanism provided by the invention, pass through multiple-objection optimization first The optimal population of algorithm search, so as to find the relationship between optimal classifier, and using mutual information index as classification The foundation of device parameter setting, so as to effectively by Cluster-Fusion into classification task, so the property of classifier can be improved Can, improve the accuracy of classification.
Further, the cluster target letter based on silhouette coefficient design that the present invention passes through a kind of quick low complex degree simultaneously Several and class object function carrys out subsidiary classification device, so as to further improve the performance of classifier.
Further, the present invention is searching for optimal population using multi-objective optimization algorithm, clusters mesh as meeting When the Pareto disaggregation of two targets of scalar functions and class object function, automatic cluster can save initialization time, reversed to learn The effective range for increasing search is practised, mutual information index is as last judgment basis, so as to improve the accuracy of classifier And search efficiency.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is the present invention Some embodiments for those of ordinary skill in the art without creative efforts, can also basis These attached drawings obtain other attached drawings.
Fig. 1 is a kind of flow diagram of the construction method of the classifier based on Cluster Classification associative mechanism of the present invention；
Fig. 2 is the schematic diagram of the classifier based on Cluster Classification associative mechanism in the present invention；
Fig. 3 is the flow chart of the classifier of the Cluster Classification mechanism optimized based on fireworks algorithm and difference algorithm.
Specific embodiment
It is an object of the invention to provide a kind of based on poly- for the bad technical problem of classifier performance in the prior art The construction method and classification method of the classifier of class classification associative mechanism, and achieve the purpose that improve classifier performance.
In order to achieve the above objectives, central scope of the invention is as follows:
A kind of construction method of classifier based on Cluster Classification associative mechanism is provided, firstly, initialization population, will cluster The distribution situation at center is encoded into population at individual, is then searched for optimal population using multi-objective optimization algorithm, is made its conduct The Pareto disaggregation for meeting two targets is concentrated from Pareto solution further according to mutual information index and selects optimal solution as joint The parameter setting of the classifier of Cluster Classification mechanism.
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Embodiment one
A kind of construction method of classifier based on Cluster Classification associative mechanism is present embodiments provided, it referring to Figure 1, should Method includes:
Step S1: the distribution situation of cluster centre is encoded into population at individual, initialization population is obtained, wherein is initial Change includes cluster objective function and class object function in population.
Specifically, step S1 is that coding obtains initial population.
In one embodiment, step S1 is specifically included:
Step S1.1: multiple populations are directed to, corresponding cluster centre distribution situation is set；
Step S1.2: coding cluster centre distribution situation, and it will be used as decision variable, according to fitness calculation formula, meter Corresponding cluster target and class object value are calculated, and using corresponding cluster target as cluster objective function, class object value As class object function, wherein shown in the concrete form such as formula (1) and formula (2) of cluster objective function and class object function:
In formula (1), f (xi) label predicted of presentation class device, yiExpression is really label, δ (f (xi), yi) indicate pre- Whether mark label are consistent with true tag, consistent results 1, inconsistent results 0, and N indicates test data total quantity；
In formula (2), nnijIndicate distance sample xiMinimum distance j-th of sample, p (ck|nnij) indicate j-th of sample Belong to the conditional probability of k-th of cluster,It is a parameter, works as nnijAnd xiWhen belonging to cluster k, it is 0, is otherwise 1/j；
Step S1.3: decision variable, cluster objective function and class object function are encapsulated into population at individual simultaneously, Multiple individual composition initialization populations.
Specifically, in step S1.1, corresponding cluster centre distribution situation can be set at random for multiple populations.Step Rapid S1.2 is that cluster objective function is designed based on the method for silhouette coefficient using quick and low complex degree, after assisting The building of continuous classifier.
Step S2: searching for optimal population using multi-objective optimization algorithm, clusters objective function as meeting and divides The Pareto disaggregation of two targets of class objective function.
Specifically, step S2 is to find optimal classifier using a kind of knockdown search Multi-object searching algorithm Interior relational matrix specifically obtains cluster centre after can clustering to training data, by cluster centre and known mark Label value finds out relational matrix.
In one embodiment, step S2 is specifically included:
Step S2.1: it in former generation, is evolved using the crossover operator and mutation operator of difference algorithm to current population Processing, obtains progeny population；
Step S2.2: in former generation, using backward learning algorithm, inverse change is carried out to progeny population, and will be reversed The population of front and back carries out non-dominated ranking after merging, and obtains reversed sub- population；
Step S2.3: in former generation, using fireworks algorithm, calculating the burst radius and spark number of reversed sub- population, And it is based on burst radius and spark number, to generate spark population in peripheral extent using reversed sub- population as fireworks population, Fireworks population and spark population carry out non-dominated ranking and generate next-generation population；
Step S2.4: using next-generation population as former generation population is worked as, step S2.1~step S2.3 is repeated, is changed For evolutionary process；
Step S2.5: when iterative evolution process meets termination condition, iterative evolution is terminated, and exports Pareto disaggregation.
Specifically, differential evolution algorithm (Differential Evolution, DE) is by Storn and Price in 1995 Year is put forward for the first time.It is mainly used for solving real number optimization problem.The algorithm is a kind of adaptive global optimization algorithm based on group, One kind for belonging to evolution algorithmic, since it has the characteristics that structure is simple, easy to accomplish, convergence is quick, strong robustness.
In evolution algorithm, when generating the population of a new generation, in order to guarantee the newly diversity for population, calculated by backward learning Method, so that the solution that population has certain small probability to select not degree of being accommodated preference.Thus, it is more preferably reversed obtained in step S2.2 Sub- population.
For the chromosome not dominated mutually, claiming these chromosomes to be in same layer, then all chromosome can be divided It is exactly the sort algorithm for being layered chromosome to several layers non-dominated ranking, the layer got is known as the non-dominant layer of the first order, and second Wherein the non-dominant layer of the first order is in the forward position Pareto (Pareto Front) to the non-dominant layer ... of grade.
Fireworks algorithm (Fireworks Algorithm), is abbreviated as FWA, be by the night sky fireworks explode inspiration and A kind of Swarm Intelligence Algorithm proposed.Fireworks algorithm starts iteration, successively using explosion operator, mutation operator, mapping ruler and Selection strategy, until reaching termination condition, that is, meet the required precision of problem or reaching maximal function assessment number.
The realization of fireworks algorithm includes following several steps:
1) some fireworks are randomly generated in specific solution space, each fireworks represents a solution of solution space.
2) fitness value of each fireworks is calculated according to fitness function, and spark is generated according to fitness value.Spark Number calculated based on the thought of the immune concentration in immunology, i.e., the better fireworks of fitness value produce pyrophoric number more It is more.
3) it according to the fireworks attribute in reality and the actual conditions of combination search problem, is generated in the radiation space of fireworks Spark.(size of the explosion amplitude of some fireworks determines that fitness value is bigger by fitness value of the fireworks on function, quick-fried Fried amplitude is bigger, and vice versa).Each spark represents a solution in solution space.In order to guarantee the diversity of population, need It is suitably made a variation to fireworks, such as Gaussian mutation.
4) optimal solution for calculating population, determines whether to meet the requirements, and stops search if meeting, does not meet, continue Iteration.The initial value of iteration is the other solutions for the best solution and selection that this circulation obtains.
Wherein, the termination condition in step S2.5 includes: that the number of iterations satisfaction reaches maximum number of iterations and population is whole The variation of body is less than the minimum population variation of setting.
Step S3: optimal solution is selected from Pareto solution concentration according to mutual information index, and optimal according to what is selected Solution, the parameter of classifier of joint Cluster Classification mechanism is configured, then the building Cluster Classification connection of the parameter based on setting The classifier of conjunction mechanism.
Wherein, step S3 is specifically included:
Step S3.1: the association relationship of all Pareto disaggregation is calculated；
Step S3.2: according to the size of association relationship, the maximum Pareto disaggregation of association relationship is selected as optimal Solution is configured the parameter of the classifier of joint Cluster Classification mechanism；
Step S3.3: the classifier of the parameter building Cluster Classification associative mechanism based on setting.
Specifically, Pareto optimal solution, also referred to as Pareto efficiency (Pareto efficiency) refer to resource point A kind of perfect condition matched, it is assumed that intrinsic group and assignable resource, from a kind of distribution state to another state In variation, under the premise of not making anyone circumstances degenerate, so that at least one people becomes more preferable.Pareto-optimality is just It is the leeway that impossible have more Pareto improvements again.
Mutual information is a kind of useful measure information in information theory, it can regard the pass for including in a stochastic variable as In the information content of another stochastic variable, or perhaps a stochastic variable is reduced not due to another known stochastic variable Certainty.Wherein, calculate the formula of mutual information be it is known, the mutual information index in the embodiment of the present invention refers to prediction bid Association relationship between the test data and truthful data of label, for this value closer to 1, classification accuracy is higher.
After the present invention obtains one group of Pareto disaggregation by evolution algorithmic, optimal solution is therefrom chosen by step S3, is pressed It is ranked up according to association relationship size, parameter of the solution of maximum mutual information as classifier is selected, to obtain optimal classification Device setting.
In one embodiment, after step S3.3, the method also includes step S3.4:
It is tested using classifier of the test data to building.
Specifically, in order to be further improved classifier, the present invention also passes through test data and tests corresponding data set feelings Condition.It if test result does not meet preset condition, can must sort according to association relationship, successively choose the parameter met the requirements and arrive It is tested in classifier.
Fig. 2 is referred to, for the schematic diagram of the classifier based on Cluster Classification associative mechanism in the present invention, when learning classifier It, can be with the quality of testing classification device by test data by the available optimal parameter of training data after general framework.Its Middle training process constructs the process of classifier.
Fig. 3 is the flow chart of the classifier of the Cluster Classification mechanism optimized based on fireworks algorithm and difference algorithm, is first Data collection is carried out, the number of cluster centre is then assessed, two meta templates are set, is initialized after random value is set to individual Population, upper right portion indicate to search for optimal population using multi-objective optimization algorithm (fireworks difference combinational algorithm).
Embodiment two
Based on same inventive concept, a kind of classification based on the classifier constructed in embodiment one is present embodiments provided Method, the classification method include:
After the cluster centre and clustering relationships that calculate data to be processed, classification results are obtained by classifier.
The beneficial effect of the method provided in order to further illustrate the present invention, below by several specific examples to this hair The classifier of bright building is compared for classifying with existing method, wherein table 1 is the present invention on 19 UCI data sets Classification accuracy and the comparison with other algorithms, table 2 be the present invention 4 image segmentation standards test libraries mutual information index And the comparison with other algorithms, MSCC-DE/FWA is the method for use of the invention, from Tables 1 and 2, it will be seen that, it is of the invention The available preferable classifying quality of method.
Classification accuracy on 1 19 UCI data sets of table and the comparison with other algorithms
Table 24 image segmentation standards test libraries mutual information index and with the comparison of other algorithms
NMI MASCC-DE/FWA MOASCC MSCC SVM RBFNN semi-MOCK
two categories image 0.963 0.959 0.905 0.932 0.897 0.957
Three categories image 0.971 0.973 0.914 0.892 0.953 0.878
four categories image 0.977 0.982 0.924 0.947 0.862 0.94
two categories image 0.981 0.974 0.932 0.921 0.903 0.917
In general, it is proposed that be a kind of automatic categorizer based on Cluster Classification associative mechanism.Firstly, proposing A kind of knockdown search Multi-object searching algorithm finds the relational matrix in optimal classifier.Secondly, proposing a kind of fast The cluster objective function based on silhouette coefficient design of speed while low complex degree is come subsidiary classification device.Finally, in Cluster Classification machine Done 3 points of improvement again in system the inside: automatic cluster saves initialization time, and backward learning increases the effective range of search, mutual information Index is as last judgment basis.The algorithm can be effectively by Cluster-Fusion into classification task the inside, thus the standard of classifier Exactness and search efficiency.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic Property concept, then additional changes and modifications can be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, those skilled in the art can carry out various modification and variations without departing from this hair to the embodiment of the present invention The spirit and scope of bright embodiment.In this way, if these modifications and variations of the embodiment of the present invention belong to the claims in the present invention And its within the scope of equivalent technologies, then the present invention is also intended to include these modifications and variations.

Claims (7)

1. a kind of construction method of the classifier based on Cluster Classification associative mechanism characterized by comprising
Step S1: the distribution situation of cluster centre is encoded into population at individual, initialization population is obtained, wherein initialization kind It include cluster objective function and class object function in group；
Step S2: searching for optimal population using multi-objective optimization algorithm, clusters objective function and classification mesh as meeting The Pareto disaggregation of two targets of scalar functions；
Step S3: selecting optimal solution from Pareto solution concentration according to mutual information index, and according to the optimal solution selected, The parameter of the classifier of joint Cluster Classification mechanism is configured, then the parameter based on setting constructs Cluster Classification associative mechanism Classifier.
2. the method as described in claim 1, which is characterized in that step S1 is specifically included:
Step S1.1: multiple populations are directed to, corresponding cluster centre distribution situation is set；
Step S1.2: coding cluster centre distribution situation, and will be calculated as decision variable according to fitness calculation formula Corresponding cluster target and class object value, and using corresponding cluster target as cluster objective function, class object value conduct Class object function, wherein shown in the concrete form such as formula (1) and formula (2) of cluster objective function and class object function:
In formula (1), f (xi) label predicted of presentation class device, yiExpression is really label, δ (f (xi), yi) indicate pre- mark , consistent results 1 whether consistent with true tag, inconsistent results 0 are signed, N indicates test data total quantity；
In formula (2), nnijIndicate distance sample xiMinimum distance j-th of sample, p (ck|nnij) indicate that j-th of sample belongs to The conditional probability of k-th of cluster,It is a parameter, works as nnijAnd xiWhen belonging to cluster k, it is 0, is otherwise 1/i；
Step S1.3: decision variable, cluster objective function and class object function are encapsulated into population at individual simultaneously, multiple Individual composition initialization population.
3. the method as described in claim 1, which is characterized in that step S2 is specifically included:
Step S2.1: in former generation, doing evolution processing to current population using the crossover operator and mutation operator of difference algorithm, Obtain progeny population；
Step S2.2: in former generation, using backward learning algorithm, inverse change is carried out to progeny population, and will reversed front and back Population merge after carry out non-dominated ranking, obtain reversed sub- population；
Step S2.3: in former generation, using fireworks algorithm, the burst radius and spark number of reversed sub- population, and base are calculated In burst radius and spark number, to generate spark population, fireworks in peripheral extent using reversed sub- population as fireworks population Population and spark population carry out non-dominated ranking and generate next-generation population；
Step S2.4: using next-generation population as work as former generation population, repeat step S2.1~step S2.3, be iterated into Change process；
Step S2.5: when iterative evolution process meets termination condition, iterative evolution is terminated, and exports Pareto disaggregation.
4. method as claimed in claim 3, which is characterized in that the termination condition in step S2.5 includes: that the number of iterations meets Reach maximum number of iterations and whole the changing of population is less than the minimum population variation of setting.
5. the method as described in claim 1, which is characterized in that step S3 is specifically included
Step S3.1: the association relationship of all Pareto disaggregation is calculated；
Step S3.2: according to the size of association relationship, selecting the maximum Pareto disaggregation of association relationship as optimal solution, right The parameter of the classifier of joint Cluster Classification mechanism is configured；
Step S3.3: the classifier of the parameter building Cluster Classification associative mechanism based on setting.
6. method as claimed in claim 5, which is characterized in that after step S3.3, the method also includes step S3.4:
It is tested using classifier of the test data to building.
7. a kind of classification method based on classifier constructed by any one of claim 1 to 5 claim, which is characterized in that The classification method includes:
After the cluster centre and clustering relationships that calculate data to be processed, classification results are obtained by classifier.
CN201910652753.3A 2019-07-19 2019-07-19 The construction method and classification method of classifier based on Cluster Classification associative mechanism Pending CN110490234A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111325284A (en) * 2020-03-09 2020-06-23 武汉大学 Self-adaptive learning method and device based on multi-target dynamic distribution
CN112654081A (en) * 2020-12-14 2021-04-13 西安邮电大学 User clustering and resource allocation optimization method, system, medium, device and application
CN114239742A (en) * 2021-12-21 2022-03-25 中国人民解放军国防科技大学 Medical data classification method based on rule classifier and related equipment

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111325284A (en) * 2020-03-09 2020-06-23 武汉大学 Self-adaptive learning method and device based on multi-target dynamic distribution
CN112654081A (en) * 2020-12-14 2021-04-13 西安邮电大学 User clustering and resource allocation optimization method, system, medium, device and application
CN112654081B (en) * 2020-12-14 2023-02-07 西安邮电大学 User clustering and resource allocation optimization method, system, medium, device and application
CN114239742A (en) * 2021-12-21 2022-03-25 中国人民解放军国防科技大学 Medical data classification method based on rule classifier and related equipment
CN114239742B (en) * 2021-12-21 2024-04-02 中国人民解放军国防科技大学 Medical data classification method based on rule classifier and related equipment

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Application publication date: 20191122