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Data feature selection method and system based on improved particle swarm algorithm

A technology to improve particle swarm and particle swarm algorithm, applied in computing, computer parts, instruments, etc., can solve problems such as premature convergence, performance impact of particle swarm algorithm, lack of diversity, etc., achieve less adjustable parameters and improve convergence Speed ​​and convergence accuracy, the effect of a small number of feature subsets

Inactive Publication Date: 2019-01-04
SHANDONG UNIV
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Problems solved by technology

However, the performance of the particle swarm optimization algorithm is easily affected by its own parameter settings, and there are problems such as premature convergence, lack of diversity, and easy to fall into local optimum.

Method used

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  • Data feature selection method and system based on improved particle swarm algorithm
  • Data feature selection method and system based on improved particle swarm algorithm
  • Data feature selection method and system based on improved particle swarm algorithm

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Embodiment Construction

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0050] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinatio...

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Abstract

The invention discloses a data feature selection method and a system based on an improved particle swarm algorithm, comprising the following steps: a classifier model of an evaluation feature subset is determined; the correct classification rate of the classifier model is used to guide feature selection, and the number of feature subsets is gradually added to guide feature selection, and the quality evaluation function of feature subsets is established; criteria for feature selection in a dataset are determined; the particle swarm optimization (PSO) algorithm is improved. According to the quality evaluation function of the feature subset, the fitness of each particle in the PSO is calculated, and the inertia weight of the PSO algorithm is updated by Logistic chaotic atlas, so that the improved PSO algorithm can process the data set and obtain the selection results.

Description

technical field [0001] The invention relates to a data feature selection method and system based on an improved particle swarm algorithm. Background technique [0002] With the rapid development of information industry and science and technology, the accumulation of data volume is also increasing. In the face of rapidly growing data, quickly and efficiently mining useful data features for social development has become a key problem that needs to be solved urgently. Feature selection has the advantages of reducing data dimensions, improving model performance, reducing overfitting, and enhancing data connections. It can solve data mining problems to the greatest extent, extract high-value features, and dig out useful information hidden in the data. . The purpose of feature selection is to remove irrelevant and redundant data features to the greatest extent without reducing the accuracy of the classifier, that is, to find the best feature subset from the original data set. F...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2111G06F18/24147
Inventor 周风余陈科尹磊王玉刚万方汪佳宇边钧健刘进
Owner SHANDONG UNIV
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