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Optimal and Novel Hybrid Feature Selection Framework for Effective Data Classification

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Advances in Systems, Control and Automation

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 442))

Abstract

Data mining methods are frequently applied in the framework of data classification. Under data mining methods, feature selection (FS) algorithms are essential for dealing with various dimensional data sets that may contain features in the range of small, medium, and large dimensions. Handling large number of features always raises the issues regarding the classifier accuracy and running time. A novel hybrid feature selection technique build on symmetrical uncertainty and genetic algorithm is proposed. The experiments’ results on UCI datasets using this hybrid framework proved that proposed feature selector is efficient through minimizing the volume of initial features and accurate by providing better detection performance in the classification algorithms comparing with other feature selectors in the literature. It is evident from the earlier research work the prosed method promotes in optimizing and improves the performance. In summary, the proposed feature selection method has outperformed other methods in minimizing the selected features, classification performance and reduces the executing time.

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Author information

Authors and Affiliations

  1. Faculty of Computing, Botho University, Gaborone, Botswana

    Sivakumar Venkataraman

  2. Department of Information Systems, BIUST, Gaborone, Botswana

    Sivakumar Venkataraman &  Rajalakshmi Selvaraj

Authors
  1. Sivakumar Venkataraman
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  2. Rajalakshmi Selvaraj
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Corresponding authors

Correspondence to Sivakumar Venkataraman or Rajalakshmi Selvaraj .

Editor information

Editors and Affiliations

  1. Clinical Engineering, University of Virginia Health System, Charlottesville, Virginia, USA

    Avinash Konkani

  2. Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology(SMIT), Rangpo, Sikkim, India

    Rabindranath Bera

  3. Department of Energy Engineering, North-Eastern Hill University, Shillong, Megalaya, India

    Samrat Paul

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Venkataraman, S., Rajalakshmi Selvaraj (2018). Optimal and Novel Hybrid Feature Selection Framework for Effective Data Classification. In: Konkani, A., Bera, R., Paul, S. (eds) Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol 442. Springer, Singapore. https://doi.org/10.1007/978-981-10-4762-6_48

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  • DOI: https://doi.org/10.1007/978-981-10-4762-6_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4761-9

  • Online ISBN: 978-981-10-4762-6

  • eBook Packages: EngineeringEngineering (R0)

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