Cancer Classification using SVM-boosted Multiobjective Differential Fuzzy Clustering

Authors:
Indrajit Saha;Ujjwal Maulik;Sanghamitra Bandyopadhyay;Dariusz Plewczynski
Affiliations:
ICM, University of Warsaw, Warsaw, Poland;Dept. of Comp. Sci. and Engg., Jadavpur University, Kolkata, West Bengal, India;Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India;ICM, University of Warsaw, Warsaw, Poland
Venue:
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Year:
2010

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Abstract

Microarray technology facilitates the monitoring of the expression profile of a large number of genes across different experimental conditions or tissue samples simultaneously. Microarray technology is being utilized in cancer diagnosis through the classification of the tissue samples. In this article, we have presented an integrated unsupervised technique for cancer classification. The proposed method is based on multiobjective differential fuzzy clustering of the tissue samples. In this regard, real coded encoding of the cluster centres is used and two fuzzy cluster validity indices are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. Each such solution has been improved by a novel technique based on Support Vector Machine (SVM) classification. Thereafter, the final clustering solution is produced by majority voting ensemble technique of all improved solutions. The performance of the proposed multiobjective clustering method has been compared to several other microarray clustering algorithms for three publicly available benchmark cancer data sets, viz., leukemia, Colon cancer and Lymphoma data to establish its superiority. Also statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method.