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  3. IRVANIZAM

A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation

IRVANIZAM IRVANIZAM, ZAKIAL VIKKI, SUTARMAN SUTARMAN, OPIM SALIM SITOMPUL

Abstract


. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets.


Keywords


classification, credit loans, fuzzy multiple-attribute decision-making, logistic regression

References


Chortareas, G.; Magkonis, G.; Zekente, K.-M. 2020. Credit risk and the business cycle: What do we know?. Int. Rev. Financ. Anal., 67 101421, DOI: 10.1016/j.irfa.2019.101421.

Bannier, C. E.; Bofinger, Y.; Rock, B. 2022. Corporate social responsibility and credit risk. Financ. Res. Lett., 44 102052, DOI: 10.1016/j.frl.2021.102052.

Naili, M; Lahrichi, Y. 2022. Banks’ credit risk, systematic determinants and specific factors: recent evidence from emerging markets. Heliyon, 8, no. 2, e08960, DOI: 10.1016/j.heliyon.2022.e08960.

Li, Y. 2019. Credit Risk Prediction Based on Machine Learning Methods. in 2019 14th International Conference on Computer Science & Education (ICCSE), pp. 1011–1013. DOI: 10.1109/ICCSE.2019.8845444.

Jinjuan, L. 2017. Research on Enterprise Credit Risk Assessment Method Based on Improved Genetic Algorithm. in 2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 215–218. DOI: 10.1109/ICMTMA.2017.0058.

Yang, P.; Wang, W.; Chen, Y. 2020. Application of Neural Network Based on Flexible Neural Tree in Personal Credit Evaluation. in 2020 12th International Conference on Advanced Computational Intelligence (ICACI), pp. 218–223. DOI: 10.1109/ICACI49185.2020.9177759.

Liu, S.; Wang, R.; Han, Y. 2021. Research on Personal Credit Evaluation Based on Machine Learning Algorithm. in 2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT), pp. 48–52. DOI: 10.1109/ISCIPT53667.2021.00016.

Lin Z; Xingzhong, B; Yajun, C; Ting, W; Fei-hu, H; Mingzhu, L; Jian, P. 2020. Research on Power Market User Credit Evaluation Based on K-Means Clustering and Contour Coefficient. in 2020 3rd International Conference on Robotics, Control and Automation Engineering (RCAE), pp. 64–68. DOI: 10.1109/RCAE51546.2020.9294725.

Dattachaudhuri, A.; Biswas, S.; Sarkar, S.; Boruah, A. N. 2020. Transparent Decision Support System for Credit Risk Evaluation: An automated credit approval system. in 2020 IEEE-HYDCON, pp. 1–5. DOI: 10.1109/HYDCON48903.2020.9242905.

Shi, H.; Wang, Z.; Wang, X. 2020. Credit Risk Intelligent Assessment Model Based on Machine Learning. in 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), pp. 735–738. DOI: 10.1109/ICETCI55101.2022.9832083.

Egger, D. J.; García Gutiérrez, R.; Mestre, J. C.; Woerner, S. 2021. Credit Risk Analysis Using Quantum Computers. IEEE Trans. Comput., 70 (12) 2136–2145, DOI: 10.1109/TC.2020.3038063.

Hassija, V.; Bansal, G.; Chamola, V.; Kumar, N.; Guizani, M. 2020. Secure Lending: Blockchain and Prospect Theory-Based Decentralized Credit Scoring Model. IEEE Trans. Netw. Sci. Eng., 7 (4) 2566–2575, 2020, DOI: 10.1109/TNSE.2020.2982488.

Li, Q. 2023. Research on Bank Credit Risk Assessment Based on BP Neural Network. in 2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME), pp. 322–326. DOI: 10.1109/ICDIIME59043.2023.00068.

Dumitrescu, E.; Hué, S.; Hurlin, C. Tokpavi, S. 2022. Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. Eur. J. Oper. Res., 297 (3) 1178–1192, DOI: 10.1016/j.ejor.2021.06.053.

Zabor, E. C.; Reddy, C. A.; Tendulkar, R. D.; Patil, S. 2022. Logistic Regression in Clinical Studies. Int. J. Radiat. Oncol., 112 (2) 271–277, DOI: 10.1016/j.ijrobp.2021.08.007.

Yoshida, R.; Hara, H.; Saluke, P. M. 2019. Sequential Importance Sampling for Logistic Regression Model. in Computational Models for Biomedical Reasoning and Problem Solving, C.-H. Chen and S.-C. S. Cheung, Eds., Hershey, PA, USA: IGI Global, pp. 231–255. DOI: 10.4018/978-1-5225-7467-5.ch009.

Yan, M. 2019. Personal Credit Rating System Based on the Logistic Regression Method. in 2019 International Conference on Economic Management and Model Engineering (ICEMME), pp. 156–163. DOI: 10.1109/ICEMME49371.2019.00040.

Dinh, T. N.; Thanh, B. P. 2022. Loan Repayment Prediction Using Logistic Regression Ensemble Learning With Machine Learning Algorithms. in 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), pp. 79–85. DOI: 10.1109/ISCMI56532.2022.10068483.

Assef, F.; Steiner, M. T.; Steiner Neto, P. J.; Franco, D. G. B. 2019. Classification Algorithms in Financial Application: Credit Risk Analysis on Legal Entities. IEEE Lat. Am. Trans., 17 (10) 1733–1740, DOI: 10.1109/TLA.2019.8986452.

Manglani R.; Bokhare, A. 2021. Logistic Regression Model for Loan Prediction: A Machine Learning Approach. in 2021 Emerging Trends in Industry 4.0 (ETI 4.0), pp. 1–6. DOI: 10.1109/ETI4.051663.2021.9619201.

Zhang Z.; Han, Y. 2020. Detection of Ovarian Tumors in Obstetric Ultrasound Imaging Using Logistic Regression Classifier With an Advanced Machine Learning Approach. IEEE Access, 8 44999–45008, DOI: 10.1109/ACCESS.2020.2977962.

Chang, C.-C.; Chen, C.-H.; Hsieh, J.-G.; Jeng, J.-H. 2021. Survival Prediction in Patients with Diffuse Large B-cell Lymphoma Using Logistic and Neural Network Models, in 2021 IEEE 3rd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), pp. 67–68. DOI: 10.1109/ECBIOS51820.2021.9510518.

Schryvers, S.; De Bock, T.; Uyttendaele, M.; Jacxsens, L. 2023. Multi-criteria decision-making framework on process water treatment of minimally processed leafy greens. Food Control, 148 p. 109661, DOI: 10.1016/j.foodcont.2023.109661.

Mendonça, G. H. M.; Ferreira, F. G. D. C.; Cardoso, R. T. N.; Martins, F. V. C. 2020. Multi-attribute decision making applied to financial portfolio optimization problem. Expert Syst. Appl., 158 p. 113527, DOI: 10.1016/j.eswa.2020.113527.

Syed Z.; Lawryshyn, Y. 2020. Multi-criteria decision-making considering risk and uncertainty in physical asset management. J. Loss Prev. Process Ind., 65 p. 104064, DOI: 10.1016/j.jlp.2020.104064.

Pasman, H. J.; Rogers, W. J.; Behie, S. W. 2022. Selecting a method/tool for risk-based decision making in complex situations. J. Loss Prev. Process Ind., 74 p. 104669, DOI: 10.1016/j.jlp.2021.104669.

Sun, R.; Gong, Z.; Gao, G.; Shah, A. A. 2020. Comparative analysis of Multi-Criteria Decision-Making methods for flood disaster risk in the Yangtze River Delta. Int. J. Disaster Risk Reduct., 51, p. 101768, DOI: 10.1016/j.ijdrr.2020.101768.

Thomas, L.; Crook, J.; Edelman, D. 2017 Credit Scoring and Its Applications, 2nd ed. Philadelphia, PA, USA: SIAM-Society for Industrial and Applied Mathematics.

Pisner, D. A.; Schnyer, D. M. 2020. Chapter 6 - Support vector machine, in Machine Learning, A. Mechelli and S. Vieira, Eds., Academic Press, pp. 101–121. DOI: 10.1016/B978-0-12-815739-8.00006-7.

Wang, Y.; Pan, Z.; Dong, J. 2022. A new two-layer nearest neighbor selection method for kNN classifier. Knowledge-Based Syst., 235 p. 107604, DOI: 10.1016/j.knosys.2021.107604.

Zadeh, L. A. 1975. The concept of a linguistic variable and its application to approximate reasoning—I, Inf. Sci. (Ny)., 8 (3) 199–249, DOI: 10.1016/0020-0255(75)90036-5.

Jiang, Y.-P.; Fan, Z.-P.; Ma, J. 2008. A method for group decision making with multi-granularity linguistic assessment information. Inf. Sci. (Ny)., 178 (4) 1098–1109, DOI: 10.1016/j.ins.2007.09.007.

Rahmani, A.; Hosseinzadeh Lotfi, F.; Rostamy-Malkhalifeh, M.; Allahviranloo, T. 2016. A New Method for Defuzzification and Ranking of Fuzzy Numbers Based on the Statistical Beta Distribution. Adv. Fuzzy Syst., 2016 p. 6945184, DOI: 10.1155/2016/6945184.

Mahmoudi, A. 2021. OPA Solver: A Solver for Multiple-Attribute Decision-Making Problems. DOI: 10.5281/ZENODO.4453887.

Jahan, A.; Edwards, K. L.; Bahraminasab, M. 2016. 5 - Multi-attribute decision-making for ranking of candidate materials. in Multi-criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design (Second Edition), A. Jahan, K. L. Edwards, and M. Bahraminasab, Eds., Second Edition.Butterworth-Heinemann, pp. 81–126. DOI: 10.1016/B978-0-08-100536-1.00005-9.

Buckland, M.; Gey, F. 1994. The relationship between Recall and Precision. J. Am. Soc. Inf. Sci., 45 (1) 12–19, DOI: 10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L.

Jerić, S.; vSarlija, N.; vSori’c, K.; Rosenzweig, V. 2009. Logistic Regression and Multicriteria Decision Making in Credit Scoring,

Kutlu Gündoğdu, F.; Kahraman, C. 2019. Spherical fuzzy sets and spherical fuzzy TOPSIS method. J. Intell. Fuzzy Syst., 36 337–352, DOI: 10.3233/JIFS-181401.

Smarandache, F. 1998 Neutrosophy: Neutrosophic Probability, Set, and Logic: Analytic Synthesis & Synthetic Analysis. American Research Press.

Jana C.; Pal, M. 2021. Extended bipolar fuzzy EDAS approach for multi-criteria group decision-making process. Comput. Appl. Math., 40 (1) p. 9, DOI: 10.1007/s40314-020-01403-4.

Irvanizam, I.; Syahrini, I.; Afidh, R. P. F.; Andika, M. R.; Sofyan, H. 2019. Applying Fuzzy Multiple-Attribute Decision Making Based on Set-pair Analysis with Triangular Fuzzy Number for Decent Homes Distribution Problem. in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, DOI: 10.1109/CITSM.2018.8674290.

Irvanizam, I.; Nazaruddin, N.; Syahrini, I. 2018. Solving Decent Home Distribution Problem Using ELECTRE Method with Triangular Fuzzy Number. in Proceedings of ICAITI 2018 - 1st International Conference on Applied Information Technology and Innovation: Toward A New Paradigm for the Design of Assistive Technology in Smart Home Care, DOI: 10.1109/ICAITI.2018.8686768.

Irvanizam, I.; Marzuki, M.; Patria, I.; Abubakar, R. 2018. An Application for Smartphone Preference Using TODIM Decision Making Method. in Proceedings - 2nd 2018 International Conference on Electrical Engineering and Informatics, ICELTICs 2018, DOI: 10.1109/ICELTICS.2018.8548820.


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DOI: 10.24815/jn.v23i3.32467

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