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Feature selection method based on improved suburb wolf optimization algorithm

A feature selection method and optimization algorithm technology, applied in the direction of calculation, calculation model, computer parts, etc., can solve problems affecting the engineering application effect of feature selection algorithm, etc.

Inactive Publication Date: 2020-12-15
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

It has greatly affected the engineering application effect of feature selection algorithms in large data volume feature selection problems

Method used

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  • Feature selection method based on improved suburb wolf optimization algorithm
  • Feature selection method based on improved suburb wolf optimization algorithm
  • Feature selection method based on improved suburb wolf optimization algorithm

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Embodiment Construction

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0076] Please refer to the attached figure 1 , the invention provides a kind of feature selection method based on improved coyote optimization algorithm, comprises the steps:

[0077] S10: Obtain features to be selected from the data set.

[0078] S20: Initialize the coyote population and divide the population into N p subgroups, each subgroup contains N c According to the following formula, the social conditions of the cth coyote in the pth subgroup at the...

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Abstract

The invention discloses a feature selection method based on an improved suburb wolf optimization algorithm. The method comprises the following steps: obtaining features to be selected from a data set;initializing a suburb wolf population to obtain social conditions of suburb wolves; converting social conditions of the suburb wolves into binary data; calculating a fitness function value; determining a first wolf in each subgroup; calculating the cultural tendency of each subgroup; updating all suburb wolves in each subgroup; enabling each subgroup to generate a binary newborn suburb wolf; enabling each subgroup to execute a birth-death mechanism; migrating a part of suburb wolves among the subgroups; updating ages of all suburb wolves; judging whether the current number of iterations reaches a preset maximum number of iterations or not; and selecting a feature corresponding to the suburb wolf with the best social condition in the population as an optimal feature subset. The method is few in algorithm adjustment parameters, high in search efficiency, accurate in feature selection and high in self-adaptive capacity, and the optimal feature combination can still be quickly searched under the condition that excessive human parameter adjustment intervention is not needed.

Description

technical field [0001] The invention belongs to the technical field of machine learning and data mining, and more specifically relates to a feature selection method based on an improved coyote optimization algorithm. Background technique [0002] In the prior art, the coyote optimization algorithm is a continuous optimization method newly proposed in recent years, and the feature selection method based on the coyote optimization algorithm is an effective feature selection method based on a wrapper, which divides the coyote population into several subpopulations , using the influence of the head wolf of each subgroup and the influence of cultural orientation to update the social conditions of coyotes in the subgroups, using the birth-death mechanism of subgroups to enhance the diversity of the population, and using the migration of coyotes between subgroups to Enhance the information interaction between the subgroups, and use the V-shaped transfer function and a simple binary...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06K9/62
CPCG06N3/006G06F18/2415
Inventor 张志成尹建芹
Owner BEIJING UNIV OF POSTS & TELECOMM
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