|Table of Contents|

Feature Selection Method Based on Coefficient ofVariation and Maximum Feature Tree(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年01期
Page:
111-118
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Feature Selection Method Based on Coefficient ofVariation and Maximum Feature Tree
Author(s):
Xu Haifeng; Zhang Yan; Liu Jiang; Lü Danjv
School of Big Data and Intelligent Engineering,Southwest Forestry University,Kunming 650224,China
Keywords:
feature selection; feature contribution scoring; coefficient of variation; mutual
PACS:
TP3-0
DOI:
10.3969/j.issn.1001-4616.2021.01.016
Abstract:
Feature selection is a key process in data mining. Feature contribution scoring and feature optimization are its core parts. This paper proposed a CVMI(coefficient of variation and mutual of information)method that used the coefficient of variation to measure the distance between intraclass and the mutual information to measure the distance between interclass,and then applied the algorithm to the embedded feature selection method. The experiment used four UCI data sets,one set of remote sensing data and birds sound data,and tested seven different feature contribution scoring methods. The results showed that the CVMI method was more in line with the objective law of feature contribution uation. It also achieved better results compared to the other feature scoring methods. Besides,this paper also proposed a feature optimization method CVMI-RRMFT(remove redundancy of maximum feature tree)based on CVMI to construct a maximum feature tree and remove redundancy with two-neighborhood. Experiment results demonstrated that this feature optimization method effectively reduced data dimensions and improved the classification accuracy.

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Last Update: 2021-03-15