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Stable Rules Evaluation for a Rough-Set-Based Bipolar Model: A Preliminary Study for Credit Loan Evaluation

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Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

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Abstract

The modern business environment is full of uncertain and imprecise circumstances that require decision makers (DMs) to conduct informed and circumspect decisions. In this regard, rough set theory (RST) has been widely acknowledged as capable to resolve these complicated problems while relevant knowledge can be extracted—in the form of rules—for decision aids. By using those learned rules, an innovative bipolar decision model that comprises the positive (preferred) and negative (unwanted) rules, can be applied to rank alternatives based on their similarity to the positive and the dissimilarity to the negative ones. However, in some business cases (e.g., personal credit loan), applicants need to provide information (values) on all the attributes, requested by a bank. Sometimes, experienced evaluators (e.g., senior bank staff) might question the validity of some values (direct or indirect evidences) provided by an applicant. In such a case, evaluators may assign additional values to those attributes (regarded as non-deterministic ones) in a bipolar model, to examine the stability of a rule that is supported by questionable instances. How to select those rules with satisfactory stability would be an important issue to enhance the effectiveness of a bipolar decision model. As a result, the present study adopts the idea of stability factor, proposed by Sakai et al. [1], to enhance the effectiveness of a bipolar decision model, and a case of credit loan evaluation, with partially assumed values on several non-deterministic attributes, is illustrated with the discussions of potential application in practice.

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Acknowledgment

We are grateful for the funding support of the Ministry of Science and Technology of Taiwan (R.O.C.) under the grant number MOST-105-2410-H-034-019-MY2. Also, the provided data and opinions from the XY bank are appreciated.

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Authors and Affiliations

  1. Department of Banking and Finance, Chinese Culture University (SCE), Taipei, Taiwan

    Kao-Yi Shen

  2. Mathematical Sciences Section, Department of Basic Science, Faculty of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Hiroshi Sakai

  3. Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, New Taipei City, Taiwan

    Gwo-Hshiung Tzeng

Authors
  1. Kao-Yi Shen
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  2. Hiroshi Sakai
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  3. Gwo-Hshiung Tzeng
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Corresponding author

Correspondence to Kao-Yi Shen .

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Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Shen, KY., Sakai, H., Tzeng, GH. (2017). Stable Rules Evaluation for a Rough-Set-Based Bipolar Model: A Preliminary Study for Credit Loan Evaluation. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-60837-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

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