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Intelligent Engineering Informatics pp 209–219Cite as

Improving Feature Selection Using Elite Breeding QPSO on Gene Data set for Cancer Classification

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 695))

Abstract

This paper focuses on feature set selection on microarray gene expression for cancer classification. The gene expression data are incommensurate when it comes to number of genes and number of samples. This imbalance makes it important to study feature selection algorithms from the complex gene expression data. We conducted a research with quantum particle swarm optimization with elitist breeding (EBQPSO) on gene data sets. To the best of our knowledge, the exploration of the elitist is not taken into account for deep searching and classification applications with genetic data sets. Our contribution in this paper is to use EBQPSO algorithm on gene data sets for classification of cancer. The algorithm is tested with supervised and unsupervised learning approach, viz. support vector machine, J48 and neural network. The results show that EBQPSO outperforms particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) algorithms in terms of precision and recall value.

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Authors and Affiliations

  1. Symbiosis Institute of Technology, Pune, India

    Poonam Chaudhari & Himanshu Agarwal

Authors
  1. Poonam Chaudhari
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  2. Himanshu Agarwal
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Corresponding author

Correspondence to Poonam Chaudhari .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, SRMGPC, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. Departamento de Computación, CINVESTAV-IPN, Mexico City, Mexico

    Carlos A. Coello Coello

  3. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  4. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

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Chaudhari, P., Agarwal, H. (2018). Improving Feature Selection Using Elite Breeding QPSO on Gene Data set for Cancer Classification. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_22

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  • DOI: https://doi.org/10.1007/978-981-10-7566-7_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7565-0

  • Online ISBN: 978-981-10-7566-7

  • eBook Packages: EngineeringEngineering (R0)

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