Fuzzy classifier based feature reduction for better gene selection

Authors:
Mohammad Khabbaz;Kievan Kianmher;Mohammad Alshalalfa;Reda Alhajj
Affiliations:
Computer Science Dept, University of Calgary, Calgary, Alberta, Canada;Computer Science Dept, University of Calgary, Calgary, Alberta, Canada;Computer Science Dept, University of Calgary, Calgary, Alberta, Canada;Computer Science Dept, University of Calgary, Calgary, Alberta, Canada and Department of Computer Science, Global University, Beirut, Lebanon
Venue:
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Year:
2007

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Abstract

This paper presents a novel approach for identifying relevant genes by employing a fuzzy classifier. First a fuzzy classifier rule set is derived such that each rule involves a compact set of genes. Then, a correlation matrix is produced by considering the correlations between the genes in each rule. Apriori is applied on the correlation matrix to find the maximal sets of correlated genes after tuning the minimum support value. Experiments conducted on the Leukemia dataset demonstrate the effectiveness of the proposed approach in producing relevant genes.