A coevolutionary algorithm for rules discovery in data mining

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

54 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)835-864
Journal / PublicationInternational Journal of Systems Science
Volume37
Issue number12
Publication statusPublished - 10 Oct 2006
Externally publishedYes

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.

Research Area(s)

  • Classification, Data mining, Evolutionary algorithms

Citation Format(s)

A coevolutionary algorithm for rules discovery in data mining. / Tan, K. C.; Yu, Q.; Ang, J. H.
In: International Journal of Systems Science, Vol. 37, No. 12, 10.10.2006, p. 835-864.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review