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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2012: Simulated Evolution and Learning pp 93–103Cite as

Ensemble Fuzzy Rule-Based Classifier Design by Parallel Distributed Fuzzy GBML Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7673))

Abstract

We have already proposed an island model for parallel distributed implementation of fuzzy genetics-based machine learning (GBML) algorithms. As in many other island models, a population of individuals is divided into multiple subpopulations. Each subpopulation is assigned to a different island. The main characteristic feature of our model is that training patterns are also divided into multiple training data subsets. Each subset is assigned to a different island. The assigned subset is used to train the subpopulation in each island. The assignment of the training data subsets is periodically rotated over the islands (e.g., every 100 generations). A migration operation is also periodically used. Our original intention in the use of such an island model was to decrease the computation time of fuzzy GBML algorithms. In this paper, we propose an idea of using our island model for ensemble classifier design. An ensemble classifier is constructed by choosing the best classifier in each island. Since the subpopulation at each island is evolved using a different training data subset, a different classifier may be obtained from each island to construct an ensemble classifier. This suggests a potential ability of our island model as an ensemble classifier design tool. However, the diversity of the obtained classifiers from multiple islands seems to be decreased by frequent training data subset rotation and frequent migration. In this paper, we examine the effects of training data subset rotation and migration on the performance of designed ensemble classifiers through computational experiments.

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Author information

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

    Hisao Ishibuchi, Masakazu Yamane & Yusuke Nojima

Authors
  1. Hisao Ishibuchi
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  2. Masakazu Yamane
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  3. Yusuke Nojima
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Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Le Quy Don Technical University, 100 Hoang Quoc Viet Street, Cau Giay District,, Hanoi, Vietnam

    Lam Thu Bui

  2. School of Computer Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798,, Singapore, Singapore

    Yew Soon Ong

  3. Faculty of Information Technology,, Hanoi University, km9 Nguyen Trai Road, Hanoi, Vietnam

    Nguyen Xuan Hoai

  4. Graduate School of Engineering, Department of Computer Science, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  5. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

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© 2012 Springer-Verlag Berlin Heidelberg

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Ishibuchi, H., Yamane, M., Nojima, Y. (2012). Ensemble Fuzzy Rule-Based Classifier Design by Parallel Distributed Fuzzy GBML Algorithms. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_10

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  • DOI: https://doi.org/10.1007/978-3-642-34859-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34858-7

  • Online ISBN: 978-3-642-34859-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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