A Local Version of the MLEM2 Algorithm for Rule Induction

Issue title: Understanding Computers' Intelligence Celebrating the 100th Volume of Fundamenta Informaticae in Honour of Helena Rasiowa

Article type: Research Article

Authors: Grzymala-Busse, Jerzy W. | Rzasa, Wojciech

Affiliations: Department of Electrical Engineering and Computer Science, University of Kansas Lawrence, KS 66045, USA and Institute of Computer Science, Polish Academy of Sciences, 01–237 Warsaw, Poland. E-mail: jerzy@eecs.ukans.edu | Department of Computer Science, University of Rzeszow, 35–310 Rzeszow, Poland. E-mail: wrzasa@univ.rzeszow.pl

Abstract: In this paper, we present the newest version of the MLEM2 algorithm for rule induction, a basic component of the LERS data mining system. This version of the MLEM2 algorithm is based on local lower and upper approximations, and in its current formis presented in this paper for the first time. Additionally, we present results of experiments comparing the local version of the MLEM2 algorithm for rule induction with an older version of MLEM2, which was based on global lower and upper approximations. Our experiments show that the local version of MLEM2 is significantly better than the global version of MLEM2 (2% significance level, two-tailed Wilcoxon test).

DOI: 10.3233/FI-2010-265

Journal: Fundamenta Informaticae, vol. 100, no. 1-4, pp. 99-116, 2010