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Nonlinear Feature Extraction Using Evolutionary Algorithm

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Neural Information Processing (ICONIP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3316))

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Abstract

We propose a method of nonlinear feature extraction for 2-class problems. A simple sigmoid function is used to extract features that are negatively correlated to each other. To evaluate the effectiveness of the proposed method, we employ linear and non-linear support vector machines to classify using the extracted feature sets and the original feature sets. Comparison on 4 datasets shows that our method is effective for nonlinear feature extraction.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798

    E. K. Tang & Ponnuthurai Nagaratnan Suganthan

  2. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Xin Yao

Authors
  1. E. K. Tang
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  2. Ponnuthurai Nagaratnan Suganthan
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  3. Xin Yao
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Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Electronics and Communication Sciences Unit, Kolkata, India

    Nikhil Ranjan Pal

  2. School of Computer and Information Sciences, Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Private Bag 92006, Auckland, New Zealand

    Nik Kasabov

  3. Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt-lake Campus, 700098, Calcutta, India

    Rajani K. Mudi

  4. Indian Statistical Institute, 203 B. T. Road, 700 108, Calcutta,  

    Srimanta Pal

  5. Indian Statistical Institute, Computer Vision and Pattern Recognition Unit, 700108, Kolkata, India

    Swapan Kumar Parui

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© 2004 Springer-Verlag Berlin Heidelberg

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Tang, E.K., Suganthan, P.N., Yao, X. (2004). Nonlinear Feature Extraction Using Evolutionary Algorithm. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_157

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  • DOI: https://doi.org/10.1007/978-3-540-30499-9_157

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23931-4

  • Online ISBN: 978-3-540-30499-9

  • eBook Packages: Springer Book Archive

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