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Classifier Ensemble Selection Based on mRMR Algorithm and Diversity Measures: An Application of Medical Data Classification

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Soft Computing Applications (SOFA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 633))

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Abstract

Classifier selection is a significant problem in machine learning to reduce the computational time and the number of ensemble members. Over the past decade, multiple classifier systems (MCS) have been actively exploited to enhance the classification accuracy. Finding a pertinent objective function for measuring the competence of base classifier is a critical issue to select the appropriate subset from a pool of classifiers. Along with the accuracy, diversity measures are designed as objective functions for ensemble selection. This current work proposed a new selection method based on accuracy and diversity in order to achieve better medical data classification performance. The classifiers correlation was calculated using Minimum Redundancy Maximum Relevance (mRMR) method based on relevance and diversity measures. Experiments were carried out on five data sets from UCI Machine Learning Repository and LudmilaKuncheva Collection. The experimental results proved the superiority of the proposed classifiers selection method.

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Author information

Authors and Affiliations

  1. Labged Laboratory, Annaba, Algeria

    Soraya Cheriguene & Nabiha Azizi

  2. Computer Science Department, BadjiMokhtar University, PO BOX 12, 23000, Annaba, Algeria

    Soraya Cheriguene & Nabiha Azizi

  3. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  4. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  5. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Corina A. Mnerie

  6. Vasile Goldis Western University of Arad, Arad, Romania

    Teodora Olariu

  7. College of Information & Engineering, Wenzhou Medical University, Wenzhou, People’s Republic Of China

    Fuqian Shi

Authors
  1. Soraya Cheriguene
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  2. Nabiha Azizi
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  3. Nilanjan Dey
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  4. Amira S. Ashour
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  5. Corina A. Mnerie
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  6. Teodora Olariu
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  7. Fuqian Shi
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Corresponding author

Correspondence to Nilanjan Dey .

Editor information

Editors and Affiliations

  1. Department of Automatics and Applied Informatics, Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

  3. Department of Automatics and Applied Informatics, Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Marius Mircea Balas

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Cheriguene, S. et al. (2018). Classifier Ensemble Selection Based on mRMR Algorithm and Diversity Measures: An Application of Medical Data Classification. In: Balas, V., Jain, L., Balas, M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-62521-8_32

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  • DOI: https://doi.org/10.1007/978-3-319-62521-8_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62520-1

  • Online ISBN: 978-3-319-62521-8

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