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Soft-Voting Clustering Ensemble

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Multiple Classifier Systems (MCS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7872))

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Abstract

Clustering ensemble is a framework for combining multiple based clustering results of a set of objects without accessing the original feature of the objects. The majority voting method is widely used in clustering ensemble because of its simplicity, robustness and stability. In general, the existing voting methods only accept hard clustering results as input. In this paper we propose a new algorithm, Soft-Voting Clustering Ensemble (SVCE), which has better flexibility and generalization. The theory of SVCE is illustrated and the algorithm of SVCE is stated in detail firstly. Then 15 UCI datasets are used for the experiment and the results show that the proposed method has a better performance than state of the art ensemble methods in most cases, such as Majority Voting, Weighted Majority Voting, CSPA, MCLA, HGPA.

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Author information

Authors and Affiliations

  1. School of Information Science & Technology, Southwest Jiaotong University, Chengdu, 610031, P.R. China

    Haishen Wang, Yan Yang, Hongjun Wang & Dahai Chen

Authors
  1. Haishen Wang
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  2. Yan Yang
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  3. Hongjun Wang
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  4. Dahai Chen
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Editor information

Editors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, 210046, Nanjing, China

    Zhi-Hua Zhou

  2. Dept. of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

  3. Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK

    Josef Kittler

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Wang, H., Yang, Y., Wang, H., Chen, D. (2013). Soft-Voting Clustering Ensemble. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_27

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  • DOI: https://doi.org/10.1007/978-3-642-38067-9_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38066-2

  • Online ISBN: 978-3-642-38067-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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