Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm

Authors:
Marek Sikora;Łukasz Wróbel
Affiliations:
Silesian University of Technology, Gliwice, Poland and Institute of Innovative Technologies EMAG, Katowice, Poland;Silesian University of Technology, Gliwice, Poland
Venue:
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Year:
2011

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Abstract

The proposition of adaptive selection of rule quality measures during rules induction is presented in the paper. In the applied algorithm the measures decide about a form of elementary conditions in a rule premise and monitor a pruning process. An influence of filtration algorithms on classification accuracy and a number of obtained rules is also presented. The analysis has been done on twenty one benchmark data sets.