A global search algorithm for attributes reduction

Authors:
Songbo Tan
Affiliations:
Software Department, Institute of Computing Technology, CAS, P R China Graduate School, Chinese Academy of Sciences, P R China
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

Attributes reduction is a crucial problem in rough set application to data mining In this paper, we introduce the Universal RED problem model, or UniRED, which transforms the discrete attributes reduction problems on Boolean space into continuous global optimization problems on real space Based on this transformation, we develop a coordinate descent algorithm RED2.1 for attributes reduction problems In order to examine the efficiency of our algorithms, we conduct the comparison between our algorithm RED2.1 and other reduction algorithms on some problems from UCI repository The experimental results indicate the efficiency of our algorithm.