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A Neighbourhood Based Hybrid Genetic Search Model for Feature Selection

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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Abstract

The paper presents a hybrid genetic search model (HGSM) with novel neighbourhood based uniform local search to select the subset of salient features removing redundant information from the universe of discourse. The method uses least square regression error as the fitness function for selecting the most feasible set of features from a large number of feature set. Proposed work is validated using our simulated character dataset and some real world datasets available in UCI Machine learning repository and performance comparison of proposed method with some other state of art feature selection methods are provided.

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Author information

Authors and Affiliations

  1. Neotia Institute of Technology, Management and Science, Jhinga, Diamond Harbour, South 24 Pargana, 743368, India

    Sunanda Das

  2. Purabi Das School of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah-03, India

    Arka Ghosh

  3. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah-03, India

    Asit Kumar Das

Authors
  1. Sunanda Das
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  2. Arka Ghosh
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  3. Asit Kumar Das
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Corresponding author

Correspondence to Arka Ghosh .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Das, S., Ghosh, A., Das, A.K. (2015). A Neighbourhood Based Hybrid Genetic Search Model for Feature Selection. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_37

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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