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Ensemble Models of Learning Vector Quantization Based on Bootstrap Resampling

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Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9887))

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Abstract

The purpose of this study is to improve the classification accuracy and stability of learning vector quantization using ensemble learning. We focused on an ensemble learning algorithm based on bootstrap resampling; this algorithm has been widely used in recent years. LVQs were extended to the ensemble model using three similar approaches: bagging, random forest, and double bagging. Through computational experiments using benchmark data, we investigated the compatibility between each approach and LVQ. The results showed that the double bagging approach was superior in ensemble LVQ.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant-in-Aig for Young Scientists (B) Numbers 15K1625.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Aoyama Gakuin University, 5-10-1, Fuchinobe, Chuo-ku, Sagamihara, Kanagawa, Japan

    Fumiaki Saitoh

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  1. Fumiaki Saitoh
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Correspondence to Fumiaki Saitoh .

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Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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Saitoh, F. (2016). Ensemble Models of Learning Vector Quantization Based on Bootstrap Resampling. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_32

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  • DOI: https://doi.org/10.1007/978-3-319-44781-0_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44780-3

  • Online ISBN: 978-3-319-44781-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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