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Abstract
One of the major challenges in data mining is the extraction of comprehensible knowledge from recorded data. In this paper, a coevolutionary-based classification technique, namely COevolutionary Rule Extractor (CORE), is proposed to discover classification rules in data mining. Unlike existing approaches where candidate rules and rule sets are evolved at different stages in the classification process, the proposed CORE coevolves rules and rule sets concurrently in two cooperative populations to confine the search space and to produce good rule sets that are comprehensive. The proposed coevolutionary classification technique is extensively validated upon seven datasets obtained from the University of California, Irvine (UCI) machine learning repository, which are representative artificial and real-world data from various domains. Comparison results show that the proposed CORE produces comprehensive and good classification rules for most datasets, which are competitive as compared with existing classifiers in literature. Simulation results obtained from box plots also unveil that CORE is relatively robust and invariant to random partition of datasets.
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- Citation information:
- Available online: 23 Feb 2007
Author affiliations
- a Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576
Author biographies
K. C. Tan received the B.Eng. degree with first class honors in electronics and electrical engineering and the PhD degree from the University of Glasgow, Glasgow, Scotland, in 1994 and 1997, respectively. He is currently an Associate Professor in the Department of Electrical and Computer Engineering, National University of Singapore. Dr. Tan has authored or coauthored more than 130 journals and conference publications and has served as a program committee or organizing member for many international conferences. He currently holds Associate Editor appointment in IEEE Transactions on Evolutionary Computation and International Journal of Systems Science. His current research interests include computational intelligence, evolutionary computing, and engineering design optimization.
Q. Yu received the B.E. degree from Zhejiang University, Hangzhou, China, in 2001 and the M.E. degree in electrical and computer engineering from National University of Singapore, Singapore, in 2003. He is currently pursuing his Ph.D. degree in computer science at Virginia Tech, Blacksburg, Virginia. He is a research assistant in the E-Commerce and E-Government Laboratory in Virginia Tech. His research interests include several areas in databases, with particular emphasis on Web service query optimization and rule-based data classification.
J. H. Ang received the B.Eng. degree in Electrical Engineering from the National University of Singapore, Singapore, in 2004. He is currently pursuing his PhD degree at the Centre for Intelligent Control, National University of Singapore. His research interest centers on applying computational intelligence techniques for data mining applications.
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