High-dimensional data feature selection method based on filtering method and genetic algorithm

A feature selection method and genetic algorithm technology, applied in the field of data mining, can solve the problems of high probability of deleting useful features, inappropriate high-dimensional, small sample data, etc., and achieve the effect of high computational cost and high accuracy.

Inactive Publication Date: 2018-11-13
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0003] The method introduced above has limitations such as easy to fall into local optimum and high probability

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  • High-dimensional data feature selection method based on filtering method and genetic algorithm
  • High-dimensional data feature selection method based on filtering method and genetic algorithm
  • High-dimensional data feature selection method based on filtering method and genetic algorithm

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Embodiment Construction

[0023] For the parts that are not described in detail in this embodiment, please refer to the description of the summary of the invention.

[0024] like figure 1 As shown, a high-dimensional data feature selection method based on filtering method and genetic algorithm, the specific steps are as follows:

[0025] Step 1. Input the data set Gastric1, the number of samples is 144, and the number of features is 22283, of which the number of non-cardia gastric cancer samples is 72, and the number of normal samples is 72. Gastric1 (accession: GSE29272) was downloaded in the NCBI GeneExpression Omnibus (GEO) database.

[0026] Step 2, using the maximum information coefficient (MIC) to calculate the correlation between each gene expression profile feature and the class label. First, a column of gene expression profile features is recorded as a vector X, and a column of class labels is recorded as a vector Y, and an x ​​scalar in X corresponds to a y scalar in Y to form a sample. Co...

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Abstract

The invention discloses a high-dimensional data feature selection method based on a filtering method and a genetic algorithm. Traditional feature selection methods are not suitable for high-dimensional and small-sample data because they have the limitation of high probability of falling into local optimization and deleting useful features. According to the invention, firstly, the maximum information coefficient is adopted to calculate the correlation between features of input data and class marks; then, the features are sorted in descending order according to values of the correlation, a threshold value is set, and features with weak correlation are deleted; and finally, the remaining features with strong correlation are subjected to random searching optimization by the genetic algorithm to obtain an optimal feature subset. The invention can effectively carry out feature selection on high-dimensional data and realize dimension reduction. The result of feature selection has important significance for sample class judgment, and when the method is applied to gene expression profile data, the selected features also have important biological significance.

Description

technical field [0001] The invention belongs to the technical field of data mining, and relates to a high-dimensional data feature selection method based on a filtering method and a genetic algorithm. Background technique [0002] The advancement of data collection and storage technology has made organizations accumulate massive amounts of data, and how to extract useful information from it has become a huge challenge at present. High-dimensional data generally has the characteristics of data sparsity and dimensionality disaster. The sparse nature of high-dimensional data, which is mostly zero and a few have values, makes it difficult to analyze and mine the data directly. As the data dimensions (attributes) increase, the amount of computation increases exponentially, resulting in the curse of dimensionality. Through effective feature selection on high-dimensional data, features that are effective for recognition or classification are selected, thereby simplifying calculat...

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Application Information

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IPC IPC(8): G06K9/62G06N3/12
CPCG06N3/126G06F18/2111G06F18/2411
Inventor 葛瑞泉马浙萍吴卿邬惠峰徐岗
Owner HANGZHOU DIANZI UNIV
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