Igf-bagging : Information gain based feature selection for bagging

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

11 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)6247-6259
Journal / PublicationInternational Journal of Innovative Computing, Information and Control
Volume7
Issue number11
Publication statusPublished - Nov 2011

Abstract

Bagging is one of the older, simpler and better known ensemble methods. However, the bootstrap sampling strategy in bagging appears to lead to ensembles of low diversity and accuracy compared with other ensemble methods. In this paper, a new variant of bagging, named IGF-Bagging, is proposed. Firstly, this method obtains bootstrap instances. Then, it employs Information Gain (IG) based feature selection technique to identify and remove irrelevant or redundant features. Finally, base learners trained from the new sub data sets are combined via majority voting. Twelve datasets from the UCI Machine Learning Repository are selected to demonstrate the effectiveness and feasibility of the proposed method. Experimental results reveal that IGF-Bagging gets significant improvement of the classification accuracy compared with other six methods. © 2011 ICIC INTERNATIONAL.

Research Area(s)

  • Bagging, Ensemble learning, Feature selection, Information gain