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International Joint Conference on Rough Sets

IJCRS 2016: Rough Sets pp 250–260Cite as

A Decision-Theoretic Rough Set Approach to Multi-class Cost-Sensitive Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9920))

Abstract

As a kind of probabilistic rough set model, decision-theoretic rough set is usually used to deal with binary classification problems. This paper provides a new formulation of multi-class decision-theoretic rough set by combining decision-theoretic rough set model with classical cost-sensitive learning. Upper approximation, lower approximation, positive region, negative region and boundary region can be derived from the \(n\,\times \,n\) cost matrix of classical multi-class situation. The probability thresholds for three-way decisions making are defined. A cost-sensitive classification algorithm based on multi-class decision-theoretic rough set model is presented. The experimental results on several UCI data sets indicate that the proposed algorithm can get a better performance on classification accuracy and total cost.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61403200 and Natural Science Foundation of Jiangsu Province under Grant No. BK20140800.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Guojian Deng & Xiuyi Jia

Authors
  1. Guojian Deng
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  2. Xiuyi Jia
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Corresponding author

Correspondence to Xiuyi Jia .

Editor information

Editors and Affiliations

  1. Universidad Católica del Norte, Antofagasta, Chile

    Víctor Flores

  2. University of Campinas, Campinas, SP, Brazil

    Fernando Gomide

  3. University of Warsaw, Warsaw, Poland

    Andrzej Janusz

  4. Universidad del Católica del Norte, Antofagasta, Chile

    Claudio Meneses

  5. Tongji University, Shanghai, China

    Duoqian Miao

  6. University of Applied Sciences, Munich, Germany

    Georg Peters

  7. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

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

    Guoyin Wang

  9. Universidad de Chile, Santiago, Chile

    Richard Weber

  10. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

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Cite this paper

Deng, G., Jia, X. (2016). A Decision-Theoretic Rough Set Approach to Multi-class Cost-Sensitive Classification. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_23

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  • DOI: https://doi.org/10.1007/978-3-319-47160-0_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47159-4

  • Online ISBN: 978-3-319-47160-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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