Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarraydata consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary ParticleSwarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to itshigh dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crudedomain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional,the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions aresuitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capabilityof those selected subsets. As these two objective functions are conflicting in nature, they are more suitablefor multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e.,Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets withtheir classification accuracy and validated using 10 fold cross validation techniques. A detailed comparativestudy is also made to show the betterment or competitiveness of the proposed algorithm.
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