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Processing Bio-medical Data with Class-Dependent Feature Selection

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Advances in Neural Networks (WIRN 2015)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 54))

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Abstract

In this paper, we show how to select different feature subsets for different classes, i.e., class-dependent feature subsets, for biomedical data. A feature importance ranking measure, i.e., class separability measure, is used to rank features for each class and obtain class-dependent feature importance ranking. Then several feature subsets for each class are formed and an “optimal” one for each class is determined through a classifier, e.g., the support vector machine (SVM). Our method of class-dependent feature selection is applied on several biomedical data sets and compared with class-independent feature selection. The experimental result shows that our approach to class-dependent feature selection is efficient in reducing feature dimension and producing satisfactory classification accuracy.

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Author information

Authors and Affiliations

  1. Institute for Infocomm Research, 21-01 Connexis (South Tower), 1 Fusionopolis Way, 138632, Singapore

    Nina Zhou

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, 50 Nanyang Avenue, Central Area, 639798, Singapore

    Lipo Wang

Authors
  1. Nina Zhou
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  2. Lipo Wang
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Correspondence to Lipo Wang .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Zhou, N., Wang, L. (2016). Processing Bio-medical Data with Class-Dependent Feature Selection. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_30

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  • DOI: https://doi.org/10.1007/978-3-319-33747-0_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33746-3

  • Online ISBN: 978-3-319-33747-0

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