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An Improved Branch & Bound Algorithm in Feature Selection

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

Abstract

The Branch & Bound (B&B) algorithm is a globally optimal feature selection method. The high computational complexity of this algorithm is a well-known problem. The B&B algorithm constructs a search tree, and then searches for the optimal feature subset in the tree. Previous work on the B&B algorithm was focused on how to simplify the search tree in order to reduce the search complexity. Several improvements have already existed. A detailed analysis of basic B&B algorithm and existing improvements is given under a common framework in which all the algorithms are compared. Based on this analysis, an improved B&B algorithm, BBPP+, is proposed. Experimental comparison shows that BBPP+ performs best.

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Author information

Authors and Affiliations

  1. Institute of Pattern Recognition & Image processing, Shanghai Jiaotong University, Shanghai, China, 200030

    Zhenxiao Wang, Jie Yang & Guozheng Li

Authors
  1. Zhenxiao Wang
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  2. Jie Yang
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  3. Guozheng Li
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Guoyin Wang

  2. Department of Computer Science, Nanchang University, Nanchang, 330029, P.R. China

    Qing Liu

  3. Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

    Yiyu Yao

  4. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2003 Springer-Verlag Berlin Heidelberg

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Wang, Z., Yang, J., Li, G. (2003). An Improved Branch & Bound Algorithm in Feature Selection. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_90

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  • DOI: https://doi.org/10.1007/3-540-39205-X_90

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

  • eBook Packages: Springer Book Archive

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