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Cost-Sensitive Classification Based on Decision-Theoretic Rough Set Model

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Rough Sets and Knowledge Technology (RSKT 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7414))

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Abstract

A framework of cost-sensitive classification based on decision-theoretic rough set model is proposed to determine the local minimum total cost classification and the local optimal test attributes set. Based on the proposed classification strategy, a cost-sensitive classification algorithm CSDTRS is presented. CSDTRS focuses on searching for an optimal test attributes set with minimum total cost including both misclassification cost and test cost, and then determine the classification based on the optimal test attributes set. A heuristic function for evaluating the attribute is presented to determine which attribute should be added in the optimal test attributes set. Experiments on four UCI data sets are performed to show the effectiveness of the proposed classification algorithm.

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Author information

Authors and Affiliations

  1. School of Management and Engineering, Nanjing University, Nanjing, Jiangsu, 210093, P.R. China

    Huaxiong Li, Xianzhong Zhou & Jiabao Zhao

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, 210093, P.R. China

    Huaxiong Li

  3. School of Information Science, Nanjing Audit University, Nanjing, Jiangsu, 211815, P.R. China

    Bing Huang

Authors
  1. Huaxiong Li
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  2. Xianzhong Zhou
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  3. Jiabao Zhao
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  4. Bing Huang
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Editor information

Editors and Affiliations

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

    Tianrui Li

  2. Institute of Mathematics, The University of Warsaw, ul. Banacha 2, 02-097, Warsaw, Poland

    Hung Son Nguyen

  3. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Guoyin Wang

  4. Department of Electrical Engineering and Computer Science, University of Kansas, 66045 - 7621, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  5. Department of Computing and Software, McMaster University, L8S 4K1, Hamilton, Ontario, Canada

    Ryszard Janicki

  6. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  7. Software School, Dalian University of Technology, Dalian, China

    Hong Yu

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, H., Zhou, X., Zhao, J., Huang, B. (2012). Cost-Sensitive Classification Based on Decision-Theoretic Rough Set Model. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_47

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  • DOI: https://doi.org/10.1007/978-3-642-31900-6_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31899-3

  • Online ISBN: 978-3-642-31900-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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