A unified strategy of feature selection

Authors:
Peng Liu;Naijun Wu;Jiaxian Zhu;Junjie Yin;Wei Zhang
Affiliations:
School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

In the field of data mining (DM), feature selection is one of the basic strategies handling with high-dimensionality problems. This paper makes a review of current methods of feature selection and proposes a unified strategy of feature selection, which divides overall procedures of feature selection into two stages, first to determine the FIF (Feature Important Factor) of features according to DM tasks, second to select features according to FIF. For classifying problems, we propose a new method for determining FIF based on decision trees and provide practical suggestion for feature selection. Through analysis on experiments conducted on UCI datasets, such a unified strategy of feature selection is proven to be effective and efficient.