Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 3, Pages 5900-5908Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.07.026
Keywords
Data Mining; Evolutionary algorithm; Feature selection; Multi-objective optimization
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In this paper, the feature selection problem was formulated as a multi-objective optimization problem, and new criteria were proposed to fulfill the goal. Foremost, data were pre-processed with missing value replacement scheme, re-sampling procedure, data type transformation procedure, and min-max normalization procedure. After that a wide variety of classifiers and feature selection methods were conducted and evaluated. Finally, the paper presented comprehensive experiments to show the relative performance of the classification tasks. The experimental results revealed the success of proposed methods in credit approval data. In addition, the numeric results also provide guides in selection of feature selection methods and classifiers in the knowledge discovery process. (C) 2008 Elsevier Ltd. All rights reserved.
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