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Multiple-Side Multiple-Learner for Incomplete Data Classification

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

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Abstract

Selective classifier can improve classification accuracy and algorithm efficiency by removing the irrelevant attributes of data. However, most of them deal with complete data. Actual datasets are often incomplete due to various reasons. Incomplete dataset also have some irrelevant attributes which have a negative effect on the algorithm performance. By analyzing main classification methods of incomplete data, this paper proposes a Multiple-side Multiple-learner algorithm for incomplete data (MSML). MSML first obtains a feature subset of the original incomplete dataset based on the chi-square statistic. And then, according to the missing attribute values of the selected feature subset, MSML obtains a group of data subsets. Each data subset was used to train a sub classifier based on bagging algorithm. Finally, the results of different sub classifiers were combined by weighted majority voting. Experimental results on UCI incomplete datasets show that MSML can effectively reduce the number of attributes, and thus improve the algorithm execution efficiency. At the same time, it can improve the classification accuracy and algorithm stability too.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Nos.61175046 and 61203290).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, 230601, Anhui Province, China

    Yuan-ting Yan, Yan-Ping Zhang & Xiu-Quan Du

Authors
  1. Yuan-ting Yan
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  2. Yan-Ping Zhang
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  3. Xiu-Quan Du
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Corresponding author

Correspondence to Yan-Ping Zhang .

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Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

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Yan, Yt., Zhang, YP., Du, XQ. (2015). Multiple-Side Multiple-Learner for Incomplete Data Classification. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_29

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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