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Solving Fuzzy Linear Regression with Hybrid Optimization

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Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5864))

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Abstract

Fuzzy linear regression is an important tool to find the linear inexact relationship between uncertain data. We then propose a hybrid optimization method based on tabu search and harmony search as a potential way of solving fuzzy linear regression. The proposed method aims at finding a model without considering any mathematical constraints while reducing the error of the regression’s model in comparison to other methods. The experimental comparison of the results for two classes of crisp input-fuzzy output and fuzzy input-fuzzy output data sets shows the superiority of the method over the existing ones.

This work has been supported in part under Australian Research Councils Linkage Projects Funding Scheme (project number LP0561985) and Macquarie University’s International Travel Grant Scheme.

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Author information

Authors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, 2109, Australia

    M. H. Mashinchi & M. A. Orgun

  2. Department of Statistics, Faculty of Mathematics and Computer Science, Shahid Bahonar University of Kerman, Iran

    M. Mashinchi

Authors
  1. M. H. Mashinchi
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  2. M. A. Orgun
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  3. M. Mashinchi
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

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© 2009 Springer-Verlag Berlin Heidelberg

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Mashinchi, M.H., Orgun, M.A., Mashinchi, M. (2009). Solving Fuzzy Linear Regression with Hybrid Optimization. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_37

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  • DOI: https://doi.org/10.1007/978-3-642-10684-2_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10682-8

  • Online ISBN: 978-3-642-10684-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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