Open AccessEfficient information‐theoretic unsupervised feature selection
Author(s) -
Lee J.,
Seo W.,
Kim D.W.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.2476
Subject(s) - feature selection , feature (linguistics) , computer science , information theory , artificial intelligence , mutual information , pattern recognition (psychology) , data mining , information gain , selection (genetic algorithm) , mathematics , statistics , philosophy , linguistics
The method proposed in this Letter selects a feature subset that preserves the data quality in the viewpoint of information theory. Using an efficient information‐theoretic evaluation, the proposed method identifies the final feature subset significantly faster than conventional methods.