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Feature selection method based on genetic algorithm

A feature selection method and genetic algorithm technology, applied in the field of data preprocessing, can solve problems such as ignoring the impact of the final result of the initial population, and achieve the effect of high classification accuracy

Pending Publication Date: 2021-01-05
XIAN UNIV OF TECH
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Problems solved by technology

[0006] The purpose of the present invention is to provide a feature selection method based on genetic algorithm, which solves the problem that the traditional genetic algorithm feature selection method in the prior art can ignore the influence of the initial population on the final result

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Embodiment Construction

[0045] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0046] A feature selection method based on genetic algorithm of the present invention, such as figure 1 As shown, the specific steps are as follows:

[0047]Step 1. Data preprocessing. Since the data set contains possible continuous data, may contain default values, and may contain outliers. Therefore, data preprocessing is required. For continuous data, equidistant discretization is performed; for default values, the mean value of the attribute is used for filling; for outliers, box plot analysis method is used for processing.

[0048] Step 2, feature classification, such as figure 2 As shown, feature selection can be defined as the process of detecting relevant features and discarding irrelevant and redundant features, with the goal of obtaining a subset of features that can maintain or even improve the performance of the original data...

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Abstract

The invention discloses a feature selection method based on a genetic algorithm, and provides a feature selection algorithm based on combination of two-step filtering and the genetic algorithm. Characteristics are divided into four parts of strong correlation characteristics, weak correlation non-redundancy characteristics, weak correlation redundancy characteristics and non-correlation characteristics by analyzing correlation between the characteristics and categories and redundancy between the characteristics, then the four parts of characteristics are used for guiding initialization of a genetic algorithm to carry out characteristic selection, and through experiments, the characteristics of the genetic algorithm are obtained. Compared with a traditional random initialization strategy, the improved initialization strategy selection selects fewer characteristics, and higher classification accuracy is obtained.

Description

technical field [0001] The invention belongs to the technical field of data preprocessing, and relates to a feature selection method based on a genetic algorithm. Background technique [0002] With the advent of the era of big data, the continuous increase of data dimensions has resulted in the problem of "dimension explosion", and feature selection is one of the effective methods to solve this problem. Feature selection is a dimensionality reduction method that selects m features (M>m) from M features to represent the original data. Feature selection removes irrelevant and redundant features to reduce dimensionality while ensuring the performance of algorithm execution. The advantage of feature selection is to reduce the number of features, avoid overfitting, save storage space and improve the execution efficiency of the algorithm. Feature selection is widely used in image classification, taxonomy, financial and medical fields, etc. [0003] Three methods of feature s...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2111G06F18/2411G06F18/24
Inventor 周红芳郭晓杰
Owner XIAN UNIV OF TECH