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Media type:
E-Article
Title:
Feature selection based on weighted conditional mutual information
Contributor:
Zhou, Hongfang;
Wang, Xiqian;
Zhang, Yao
Published:
Emerald, 2024
Published in:
Applied Computing and Informatics, 20 (2024) 1/2, Seite 55-68
Language:
English
DOI:
10.1016/j.aci.2019.12.003
ISSN:
2210-8327;
2634-1964
Origination:
Footnote:
Description:
<jats:p>Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.</jats:p>