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Hybrid Feature Selection Using Correlation Coefficient and Particle Swarm Optimization on Microarray Gene Expression Data

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Book cover Innovations in Bio-Inspired Computing and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 424))

Abstract

Diagnosis of cancer is one of the most emerging clinical applications in microarray gene expression data. However, cancer classification on microarray gene expression data still remains a difficult problem. The main reason for this is the significantly large number of genes present relatively compared to the number of available training samples. In this paper, we propose a hybrid feature selection approach that combines the correlation coefficient with particle swarm optimization. The process of feature selection and classification is performed on three multi-class datasets namely Lymphoma, MLL and SRBCT. After the process of feature selection is performed, the selected genes are subjected to Extreme Learning Machines Classifier. Experimental results show that the proposed hybrid approach reduces the number of effective levels of gene expression and obtains higher classification accuracy and uses fewer features compared to the same experiment performed using the traditional tree-based classifiers like J48, random forest, random trees, decision stump and genetic algorithm as well.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Nagar, Coimbatore, 641112, Tamilnadu, India

    Arunkumar Chinnaswamy

  2. Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, 642003, Tamilnadu, India

    Ramakrishnan Srinivasan

Authors
  1. Arunkumar Chinnaswamy
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  2. Ramakrishnan Srinivasan
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Corresponding author

Correspondence to Arunkumar Chinnaswamy .

Editor information

Editors and Affiliations

  1. Dep. of Computer Science, VŠB – Technical Univ. of Ostrava, Ostrava, Czech Republic

    Václav Snášel

  2. (MIR Labs), Scientific Net Innov & Res Excel, Auburn, Washington, USA

    Ajit Abraham

  3. Faculty of Elec. Eng. & Comp. Sci., VÅ B - Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Pavel Krömer

  4. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  5. Fac of Info & Comm, Comp Inte & Tech Lab, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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© 2016 Springer International Publishing Switzerland

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Chinnaswamy, A., Srinivasan, R. (2016). Hybrid Feature Selection Using Correlation Coefficient and Particle Swarm Optimization on Microarray Gene Expression Data. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_20

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  • DOI: https://doi.org/10.1007/978-3-319-28031-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28030-1

  • Online ISBN: 978-3-319-28031-8

  • eBook Packages: EngineeringEngineering (R0)

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