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Feature selection with redundancy-constrained class separability

Journal Article


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Abstract


  • Scatter-matrix-based class separability is a simple and efficient

    feature selection criterion in the literature. However, the conventional

    trace-based formulation does not take feature redundancy into account and

    is prone to selecting a set of discriminative but mutually redundant features.

    In this brief, we first theoretically prove that in the context of this

    trace-based criterion the existence of sufficiently correlated features can

    always prevent selecting the optimal feature set. Then, on top of this criterion,

    we propose the redundancy-constrained feature selection (RCFS). To

    ensure the algorithm’s efficiency and scalability,we study the characteristic

    of the constraints with which the resulted constrained 0–1 optimization can

    be efficiently and globally solved. By using the totally unimodular (TUM)

    concept in integer programming, a necessary condition for such constraints

    is derived. This condition reveals an interesting special case in which qualified

    redundancy constraints can be conveniently generated via a clustering

    of features. We study this special case and develop an efficient feature selection

    approach based on Dinkelbach’s algorithm. Experiments on benchmark

    data sets demonstrate the superior performance of our approach to

    those without redundancy constraints.

Publication Date


  • 2010

Citation


  • Wang, L., Shen, C. & Zhou, L. (2010). Feature selection with redundancy-constrained class separability. IEEE Transactions on Neural Networks, 21 (5), 853-858.

Scopus Eid


  • 2-s2.0-77951937407

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1437&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/432

Has Global Citation Frequency


Number Of Pages


  • 5

Start Page


  • 853

End Page


  • 858

Volume


  • 21

Issue


  • 5

Place Of Publication


  • United States

Abstract


  • Scatter-matrix-based class separability is a simple and efficient

    feature selection criterion in the literature. However, the conventional

    trace-based formulation does not take feature redundancy into account and

    is prone to selecting a set of discriminative but mutually redundant features.

    In this brief, we first theoretically prove that in the context of this

    trace-based criterion the existence of sufficiently correlated features can

    always prevent selecting the optimal feature set. Then, on top of this criterion,

    we propose the redundancy-constrained feature selection (RCFS). To

    ensure the algorithm’s efficiency and scalability,we study the characteristic

    of the constraints with which the resulted constrained 0–1 optimization can

    be efficiently and globally solved. By using the totally unimodular (TUM)

    concept in integer programming, a necessary condition for such constraints

    is derived. This condition reveals an interesting special case in which qualified

    redundancy constraints can be conveniently generated via a clustering

    of features. We study this special case and develop an efficient feature selection

    approach based on Dinkelbach’s algorithm. Experiments on benchmark

    data sets demonstrate the superior performance of our approach to

    those without redundancy constraints.

Publication Date


  • 2010

Citation


  • Wang, L., Shen, C. & Zhou, L. (2010). Feature selection with redundancy-constrained class separability. IEEE Transactions on Neural Networks, 21 (5), 853-858.

Scopus Eid


  • 2-s2.0-77951937407

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1437&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/432

Has Global Citation Frequency


Number Of Pages


  • 5

Start Page


  • 853

End Page


  • 858

Volume


  • 21

Issue


  • 5

Place Of Publication


  • United States