Feature selection method based on binary quantum particle swarm algorithm

A feature selection method, quantum particle swarm technology, applied in the direction of calculation, calculation model, gene model, etc., can solve the problem of large amount of calculation, achieve good classification accuracy, increase diversity, and improve the effect of diversity

CN110210529APending Publication Date: 2019-09-06ZHEJIANG UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2019-09-06

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Abstract

The invention discloses a feature selection method based on a binary quantum particle swarm algorithm. The feature selection method carries out feature correlation analysis by using a maximum information coefficient, carries out feature selection processing through an improved BQPSO algorithm, and carries out classification accuracy verification by using an SVM. Experimental results of gene expression profiles show that feature selection based on the improved BQPSO algorithm is a feasible method. According to the feature selection method, a standard binary quantum particle swarm optimization algorithm is mainly improved, and a mode based on a complete learning strategy is used for calculation of a local attractor, and meanwhile the diversity of particle swarms is improved by introducing the variation thought of a genetic algorithm. Experiments show that the improved BQPSO algorithm is used for feature selection, and better classification accuracy can be obtained.
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Description

technical field

[0001] The invention belongs to the technical field of data mining, and relates to a feature selection method based on binary quantum particle swarm algorithm. Background technique

[0002] In classification problems, data sets usually contain tens of thousands of features, including those related, irrelevant and redundant features. Due to the excessively large data set, it may even reduce the classification performance, which will cause the "curse of dimensionality" . Reducing the dimensionality of a data set through feature selection is one of the ways of data dimensionality reduction.

[0003] Feature selection occupies a very important position in the field of pattern recognition, and has very high research value. On the one hand, feature selection can effectively reduce the amount of data to be processed and reduce computing overhead; on the other hand, feature selection can eliminate non-critical interference features, reduce the correlation between f...

Examples

Embodiment Construction

[0031] Such as figure 1 As shown, a feature selection method based on the binary quantum particle swarm algorithm, the specific steps are as follows:

[0032] Step 1. Input the public dataset Lymphoma, where the number of samples is 45, the number of features is 4026, the number of negative samples is 22, and the number of positive samples is 23.

[0033] Step 2. Using the maximum information coefficient (MIC) to calculate the correlation between all features and class labels. The calculation method of MIC is shown in formula (1) (2).

[0034]

[0035]

[0036] Step 3. The features are sorted according to the relevance of the MIC value, and some weakly correlated features are deleted according to the set threshold.

[0037] Step 4. Use the binary particle swarm optimization algorithm to search and optimize the remaining features to obtain the optimal feature subset. For the specific algorithm flow chart, see figure 2 .

[0038] In the BQPSO algorithm, there is no c...