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Classification Algorithm Based on Feature Selection and Samples Selection

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5552))

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Abstract

A new classification algorithm based on support vector machine and Rough set theory is proposed in the paper. We make great use of the advantages of Rough set theory in dealing with vagueness and uncertainty information, firstly select important features by attribute reduction; secondly select effective samples by rule induction; finally construct support vector classifier by the selected important features and effective samples. Thus it can reduce training samples’ dimensions, decrease training samples’ scales and noise disturbing. It can provide us with the benefits of improving support vector machine’s training speed and classification accuracy. Result of image recognition verifies its efficiency and feasibility. It also provides us an effective method to deal with the large scale and high dimensions data set.

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Authors and Affiliations

  1. College of Science, China Agricultural University, Beijing, 100083, China

    Yitian Xu, Ling Zhen, Liming Yang & Laisheng Wang

Authors
  1. Yitian Xu
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  2. Ling Zhen
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  3. Liming Yang
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  4. Laisheng Wang
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático,, CINVESTAV-IPN,, A.P. 14-740, Av.IPN 2508,, D.F., 07360,, México, México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering,, Stevens Institute of Technology,, NJ 07030,, Hoboken,, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering,, South Dakota School of Mines & Technology,, 501 E. St. Joseph Street,, SD 57701,, Rapid City,, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Xu, Y., Zhen, L., Yang, L., Wang, L. (2009). Classification Algorithm Based on Feature Selection and Samples Selection. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_71

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  • DOI: https://doi.org/10.1007/978-3-642-01510-6_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01509-0

  • Online ISBN: 978-3-642-01510-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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