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The Use of k-Means Algorithm to Improve Kernel Method via Instance Selection

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Knowledge Science, Engineering and Management (KSEM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9403))

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Abstract

The kernel method is well known for its success in solving the curse of dimension of linearly inseparable problems. But as an instance- based learning algorithm it suffers from high memory requirement and low efficiency in that it needs to store all of the training instances. And when there are noisy instances classification accuracy can suffer. In this paper we present an approach to alleviate both of the problems mentioned above by using k-means algorithm to select only k representativeness instances of the training data. And we view the selected k instances as the new data set, where the choice of the value of k is influenced by the size and the character of the data set. It turn out that with a carefully selected k we can still get a good performance while the number of the instances stored are greatly decreased.

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Authors and Affiliations

  1. Department of Computer Science and Technology, JiLin University, Changchun, 130012, China

    Lulu Wang

Authors
  1. Lulu Wang
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Correspondence to Lulu Wang .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Songmao Zhang

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Martin Wirsing

  3. Southwest University, Chongqing, China

    Zili Zhang

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© 2015 Springer International Publishing Switzerland

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Wang, L. (2015). The Use of k-Means Algorithm to Improve Kernel Method via Instance Selection. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_50

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  • DOI: https://doi.org/10.1007/978-3-319-25159-2_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25158-5

  • Online ISBN: 978-3-319-25159-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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