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How to Evaluate Three-Way Decisions Based Binary 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))

Abstract

Appropriate measures are important for evaluating the performance of a classifier. In existing studies, many performance measures designed for two-way decisions based classification are applied to three-way decisions based classification directly, which may result in an incomprehensive evaluation. However, there is a lack of systematically research on the performance measures for three-way decisions based classification. This paper introduces some numerical measures and graphical measures for three-way decisions based binary classification.

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Acknowledgements

We would like to acknowledge the support for this work from the National Natural Science Foundation of China (Grant Nos. 61403200, 61170180), Natural Science Foundation of Jiangsu Province(Grant No.BK20140800).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Xiuyi Jia

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Lin Shang

Authors
  1. Xiuyi Jia
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  2. Lin Shang
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Corresponding author

Correspondence to Xiuyi Jia .

Editor information

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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Jia, X., Shang, L. (2015). How to Evaluate Three-Way Decisions Based Binary 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_33

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_33

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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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