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QSVM: A Support Vector Machine for Rule Extraction

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Advances in Computational Intelligence (IWANN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9095))

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Abstract

Rule extraction from neural networks represents a difficult research problem, which is NP-hard. In this work we show how a special Multi Layer Perceptron architecture denoted as DIMLP can be used to extract rules from ensembles of DIMLPs and Quantized Support Vector Machines (QSVMs). The key idea for rule extraction is that the locations of discriminative hyperplanes are known, precisely. Based on ten repetitions of stratified 10-fold cross validation trials and with the use of default learning parameters we generated symbolic rules from five datasets. The obtained results compared favorably with respect to another state of the art technique applied to Support Vector Machines.

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Author information

Authors and Affiliations

  1. University of Applied Sciences of Western Switzerland, Rue de la Prairie 4, 1202, Geneva, Switzerland

    Guido Bologna

  2. Department of Computer Science, Meiji University, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan

    Yoichi Hayashi

Authors
  1. Guido Bologna
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  2. Yoichi Hayashi
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Corresponding author

Correspondence to Guido Bologna .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. Department of Electronics Technology, University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

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Bologna, G., Hayashi, Y. (2015). QSVM: A Support Vector Machine for Rule Extraction. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-19222-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19221-5

  • Online ISBN: 978-3-319-19222-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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