Unsupervised and Supervised Learning Approaches Together for Microarray Analysis

In this article, a novel concept is introduced by using both unsupervised and supervised learning. For unsupervised learning, the problem of fuzzy clustering in microarray data as a multiobjective optimization is used, which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. In this regards, a new multiobjective differential evolution based fuzzy clustering technique has been proposed. Subsequently, for supervised learning, a fuzzy majority voting scheme along with support vector machine is used to integrate the clustering information from all the solutions in the resultant Pareto-optimal set. The performances of the proposed clustering techniques have been demonstrated on five publicly available benchmark microarray data sets. A detail comparison has been carried out with multiobjective genetic algorithm based fuzzy clustering, multiobjective differential evolution based fuzzy clustering, single objective versio...

• Type: Journal
• Year: 2011
• Where: FUIN
• Authors: Indrajit Saha, Ujjwal Maulik, Sanghamitra Bandyopadhyay, Dariusz Plewczynski
• Keywords: Cryptology, FUIN 2011, Fuzzy C Means Algorithm, Support Vector Machine, Validity Indices