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CHULMIN YUN, BYONGHWA OH, JIHOON YANG+ AND JONGHO NANG
Department of Computer Science and Engineering
Sogang University
Seoul, 121-742 Korea
Many feature subset selection algorithms have been proposed and discussed for
years. However, the problem of finding the optimal feature subset from full data still remains
to be a difficult problem. In this paper, we propose novel methods to find the relevant
feature subset by using biologically-inspired algorithms such as Genetic Algorithm
and Particle Swarm Optimization. We also propose a variant of the approach considering
the significance of each feature. We verified the performance of the proposed methods by
experiments with various real-world datasets. Our feature selection methods based on the
biologically-inspired algorithms produced better performance than other methods in terms
of the classification accuracy and the feature relevance. In particular, the modified method
considering feature significance demonstrated even more improved performance.
Received December 7, 2009 revised March 29 & November 1, 2010; accepted December 15, 2010.
Communicated by Chih-Jen Lin.
* This paper was supported by the Special Research Grant of Sogang University to Jihoon Yang.
+ Corresponding author.