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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References
Sakai, H., Okuma, H., Nakata, M., Ślȩzak, D.: Stable rule extraction and decision making in rough non-deterministic information analysis. Int. J. Hybrid Intell. Syst. 8(1), 41–57 (2011)
Pawlak, Z.: Rough set theory and its applications to data analysis. Cybern. Syst. 29(7), 661–688 (1998)
Greco, S., Matarazzo, B., Słowiński, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129(1), 1–47 (2001)
Greco, S., Matarazzo, B., Słowiński, R.: Rough approximation by dominance relations. Int. J. Hybrid Intell. Syst. 17(2), 153–171 (2002)
Greco, S., Matarazzo, B., Słowiński, R.: Rough sets methodology for sorting problems in presence of multiple attributes and criteria. Eur. J. Oper. Res. 138(2), 247–259 (2002)
Błaszczyński, J., Greco, S., Słowiński, R.: Multi-criteria classification–a new scheme for application of dominance-based decision rules. Eur. J. Oper. Res. 181(3), 1030–1044 (2007)
Inuiguchi, M., Yoshioka, Y., Kusunoki, Y.: Variable-precision dominance-based rough set approach and attribute reduction. Int. J. Approx. Reason. 50(8), 1199–1214 (2009)
Shen, K.Y., Tzeng, G.H.: DRSA-based neuro-fuzzy inference systems for the financial performance prediction of commercial banks. Int. J. Fuzzy Syst. 16(2), 173–183 (2014)
Liou, J.J., Tzeng, G.H.: A dominance-based rough set approach to customer behavior in the airline market. Inf. Sci. 180(11), 2230–2238 (2010)
Shen, K.Y., Tzeng, G.H.: Fuzzy inference-enhanced VC-DRSA model for technical analysis: Investment decision aid. Int. J. Fuzzy Syst. 17(3), 375–389 (2015)
Shen, K.Y., Hu, S.K., Tzeng, G.H.: Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model. Inf. Sci. 375, 296–313 (2017)
Sakai, H., Ishibashi, R., Koba, K., Nakata, M.: Rules and apriori algorithm in non-deterministic information systems. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 328–350. Springer, Heidelberg (2008). doi:10.1007/978-3-540-89876-4_18
Ślęzak, D., Sakai, H.: Automatic extraction of decision rules from non-deterministic data systems: theoretical foundations and sql-based implementation. In: Ślęzak, D., Kim, T.H., Zhang, Y., Ma, J., Chung, K.I. (eds.) DTA 2009. CCIS, vol. 64, pp. 151–162. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10583-8_18
Kryszkiewicz, M.: Rough set approach to incomplete information systems. Inf. Sci. 112(1–4), 39–49 (1998)
Kryszkiewicz, M.: Rules in incomplete information systems. Inf. Sci. 113(3–4), 271–292 (1999)
Shen, K.Y., Tzeng, G.H.: Contextual improvement planning by fuzzy-rough machine learning: A novel bipolar approach for business analytics. Int. J. Fuzzy Syst. 18(6), 940–955 (2016)
Shen, K.Y., Tzeng, G.H.: A novel bipolar MCDM model using rough sets and three-way decisions for decision aids. In: 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems, pp. 53–58. IEEE, August 2016
Nakata, M., Sakai, H.: Lower and upper approximations in data tables containing possibilistic information. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W. (eds.) Transactions on Rough Sets VII: Commemorating the Life and Work of Zdzisław PawlakPart II. LNCS, vol. 4400, pp. 170–189. Springer, Heidelberg (2007). doi:10.1007/978-3-540-71663-1_11
Nakata, M., Sakai, H.: Applying rough sets to information tables containing possibilistic values. In: Gavrilova, M.L., Kenneth Tan, C.J., Wang, Y., Yao, Y., Wang, G. (eds.) Transactions on Computational Science II. LNCS, vol. 5150, pp. 180–204. Springer, Heidelberg (2008). doi:10.1007/978-3-540-87563-5_11
Sakai, H., Okuma, A.: Basic algorithms and tools for rough non-deterministic information analysis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 209–231. Springer, Heidelberg (2004). doi:10.1007/978-3-540-27794-1_10
Sakai, H., Wu, M., Nakata, M.: Apriori-based rule generation in incomplete information databases and non-deterministic information systems. Fundam. Inform. 130(3), 343–376 (2014)
Sakai, H.: Software tools for RNIA (Rough Non-Deterministic Information Analysis) (2016). http://www.mns.kyutech.ac.jp/~sakai/RNIA/
Shen, K.Y.: Compromise between short-and long-term financial sustainability a hybrid model for supporting R&D decisions. Sustainability 9(3), 375 (pp. 1–17) (2017). doi:10.3390/su9030375
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17(2–3), 191–209 (1990)
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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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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