Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

Authors

  • Chandra Sekhara Rao Annavarapu Department of Computer Science and Engineering, Indian School of Mines, Dhanbad-826004, Jharkhand, India
  • Suresh Dara Department of Computer Science and Engineering, Indian School of Mines, Dhanbad-826004, Jharkhand, India
  • Haider Banka Department of Computer Science and Engineering, Indian School of Mines, Dhanbad-826004, Jharkhand, India

DOI:

https://doi.org/10.17179/excli2016-481

Keywords:

cancer micro array, gene expressions, feature selection, binary PSO, classification

Abstract

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain 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 are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for 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 with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm.

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Published

2016-08-01

How to Cite

Annavarapu, C. S. R., Dara, S., & Banka, H. (2016). Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm . EXCLI Journal, 15, 460–473. https://doi.org/10.17179/excli2016-481

Issue

Vol. 15 (2016)

Section

Original articles

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