Feature subset selection within a simulated annealing data mining algorithm

Justin C. W. Debuse, Victor J. Rayward-Smith

Research output: Contribution to journalArticle

74 Citations (Scopus)


An overview of the principle feature subset selection methods isgiven. We investigate a number of measures of feature subset quality, usinglarge commercial databases. We develop an entropic measure, based upon theinformation gain approach used within ID3 and C4.5 to build trees, which isshown to give the best performance over our databases. This measure is usedwithin a simple feature subset selection algorithm and the technique is usedto generate subsets of high quality features from the databases. A simulatedannealing based data mining technique is presented and applied to thedatabases. The performance using all features is compared to that achievedusing the subset selected by our algorithm. We show that a substantialreduction in the number of features may be achieved together with animprovement in the performance of our data mining system. We also present amodification of the data mining algorithm, which allows it to simultaneouslysearch for promising feature subsets and high quality rules. The effect ofvarying the generality level of the desired pattern is alsoinvestigated.
Original languageEnglish
Pages (from-to)57-81
Number of pages25
JournalJournal of Intelligent Information Systems
Issue number1
Publication statusPublished - 1997

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