Refining Genetic Algorithm Based Fuzzy Clustering through Supervised Learning for Unsupervised Cancer Classification

Authors:
Anirban Mukhopadhyay;Ujjwal Maulik;Sanghamitra Bandyopadhyay
Affiliations:
Department of Computer Science and Engineering, University of Kalyani, Kalyani, India 741235;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India 700032;Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India 700108
Venue:
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2009

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Abstract

Fuzzy clustering is an important tool for analyzing microarray cancer data sets in order classify the tissue samples. This article describes a real-coded Genetic Algorithm (GA) based fuzzy clustering method that combines with popular Artificial Neural Network (ANN) / Support vector Machine (SVM) based classifier in this purpose. The clustering produced by GA is refined using ANN / SVM classifier to obtain improved clustering performance. The proposed technique is used to cluster three publicly available real life microarray cancer data sets. The performance of the proposed 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.