Mining multi-class datasets using genetic relation algorithm for rule reduction

Authors:
Eloy Gonzales;Shingo Mabu;Karla Taboada;Kaoru Shimada;Kotaro Hirasawa
Affiliations:
Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

This paper describes the use of a new evolutionary method named Genetic Relation Algorithm (GRA) for reducing the number of class association rules extracted by other methods such as Apriori, Genetic Network Programming(GNP), etc. The purpose is to generate a small number of class association rules in order to delete irrelevant and redundant rules. A reduced rule set has advantages as it provides only useful rules and makes its analysis more efficient. Our approach is based on evaluating the distances between rules for evolving GRA and also evaluating the distances between the data in the test set and the rules for classification. Two matching criteria are presented: complete match and partial match. The classification accuracy obtained by our method is better compared to other reported results in multi-class datasets showing an impressive reduction rate.