An empirical comparison of rule sets induced by LERS and probabilistic rough classification

Authors:
Jerzy W. Grzymala-Busse;Shantan R. Marepally;Yiyu Yao
Affiliations:
Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS and Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland;Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS;Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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Abstract

In this paper we present results of an experimental comparison (in terms of an error rate) of rule sets induced by the LERS data mining system with rule sets induced using the probabilistic rough classification (PRC). As follows from our experiments, the performance of LERS (possible rules) is significantly better than the best rule sets induced by PRC with any threshold (two-tailed test, 5% significance level). Additionally, the LERS possible rule approach to rule induction is significantly better than the LERS certain rule approach (two-tailed test, 5% significance level).