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A Moderate Attribute Reduction Approach in Decision-Theoretic Rough Set

  • Conference paper
  • First Online:
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

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

Abstract

Attribute reduction is an important topic in Decision-Theoretic Rough Set theory. To overcome the limitations of lower-approximation-monotonicity based reduct and cost minimum based reduct, a moderate attribute reduction approach is proposed in this paper, which combines the lower approximation monotonicity criterion and cost minor criterion. Furthermore, the proposed attribute reduct is searched by solving an optimization problem, and a fusion fitness function is proposed in a generic algorithm, such that the reduct is computed in a low time complexity. Experimental analysis is included to validate the theoretic analysis and quantify the effectiveness of the proposed attribute reduction algorithm. This study indicates that the optimality is not the best and sub-optimum may be the best choice.

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Acknowledgment

This work is supported by the Natural Science Foundation of China (Nos. 61100116, 71201076, 61170105, 61473157,71171107), Qing Lan Project of Jiangsu Province of China, and the Ph.D. Programs Foundation of Ministry of Education of China (20120091120004).

Author information

Authors and Affiliations

  1. School of Management and Engineering, Nanjing University, Nanjing, 210093, Jiangsu, People’s Republic of China

    Hengrong Ju, Pei Yang, Huaxiong Li & Xianzhong Zhou

  2. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, People’s Republic of China

    Hengrong Ju & Xibei Yang

  3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, People’s Republic of China

    Pei Yang

  4. Research Center for Novel Technology of Intelligent Equipments, Nanjing University, Nanjing, 210093, People’s Republic of China

    Pei Yang, Huaxiong Li & Xianzhong Zhou

Authors
  1. Hengrong Ju
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  2. Xibei Yang
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  3. Pei Yang
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  4. Huaxiong Li
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  5. Xianzhong Zhou
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Corresponding author

Correspondence to Huaxiong Li .

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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Ju, H., Yang, X., Yang, P., Li, H., Zhou, X. (2015). A Moderate Attribute Reduction Approach in Decision-Theoretic Rough Set. 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_34

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

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