Unsupervised cancer classification through SVM-boosted multiobjective fuzzy clustering with majority voting ensemble

In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic fuzzy clustering of the tissue samples. In this regard, coordinate of the cluster centers have been encoded in the chromosomes and three fuzzy cluster validity indices are simultaneously optimized. Each solution of the resultant Pareto-optimal set has been boosted by a novel technique based on Support Vector Machine (SVM) classification. Finally, the clustering information possessed by the non-dominated solutions are combined through a majority voting ensemble technique to produce the final clustering solution. 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.