Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
Medientyp:
E-Artikel
Titel:
Feature selection based on weighted conditional mutual information
Beteiligte:
Zhou, Hongfang;
Wang, Xiqian;
Zhang, Yao
Erschienen:
Emerald, 2024
Erschienen in:
Applied Computing and Informatics, 20 (2024) 1/2, Seite 55-68
Sprache:
Englisch
DOI:
10.1016/j.aci.2019.12.003
ISSN:
2634-1964;
2210-8327
Entstehung:
Anmerkungen:
Beschreibung:
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.