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Application of Parallel Distributed Implementation to Multiobjective Fuzzy Genetics-Based Machine Learning

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Intelligent Information and Database Systems (ACIIDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9011))

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Abstract

Fuzzy genetics-based machine learning is one of data mining techniques based on evolutionary computation. It can generate accurate classifiers with a small number of fuzzy if-then rules from numerical data. Its multiobjective version can provide a number of classifiers with a different tradeoff between accuracy and complexity. One major drawback of this method is the computation time when we use it for large data sets. In our previous study, we proposed parallel distributed implementation of single-objective fuzzy genetics-based machine learning which could drastically reduce the computation time. In this paper, we apply our idea of parallel distributed implementation to multiobjective fuzzy genetics-based machine learning. Through computational experiments on large data sets, we examine the effects of parallel distributed implementation on the search performance of our multiobjective fuzzy genetics-based machine learning and its computation time.

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Authors and Affiliations

  1. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka, 599-8531, Japan

    Yusuke Nojima, Yuji Takahashi & Hisao Ishibuchi

Authors
  1. Yusuke Nojima
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  2. Yuji Takahashi
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  3. Hisao Ishibuchi
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Corresponding author

Correspondence to Yusuke Nojima .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh city, Vietnam

    Ngoc Thanh Nguyen

  2. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

  3. Bina Nusantara University, Jakarta, Indonesia

    Raymond Kosala

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Nojima, Y., Takahashi, Y., Ishibuchi, H. (2015). Application of Parallel Distributed Implementation to Multiobjective Fuzzy Genetics-Based Machine Learning. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_45

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  • DOI: https://doi.org/10.1007/978-3-319-15702-3_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15701-6

  • Online ISBN: 978-3-319-15702-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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