Feature Selection and Grouping Effect Analysis for Credit Evaluation via Regularized Diagonal Distance Metric Learning

References

• Basu R, Naughton JP (2020) The real effects of financial statement recognition: Evidence from corporate credit ratings. Management Sci. 66(4):1672–1691.Link, Google Scholar
• Bhat G, Ryan SG, Vyas D (2019) The implications of credit risk modeling for banks’ loan loss provisions and loan-origination procyclicality. Management Sci. 65(5):2116–2141.Abstract, Google Scholar
• Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations Trends® Machine Learn. 3(1):1–122.Crossref, Google Scholar
• Cakir F, He K, Xia X, Kulis B, Sclaroff S (2019) Deep metric learning to rank. Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 1861–1870.Google Scholar
• Cui L, Bai L, Wang Y, Jin X, Hancock ER (2021) Internet financing credit risk evaluation using multiple structural interacting elastic net feature selection. Pattern Recognition 114:107835.Crossref, Google Scholar
• Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. Proc. 24th Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 209–216.Google Scholar
• Der M, Saul L (2012) Latent coincidence analysis: A hidden variable model for distance metric learning. Advances in Neural Information Processing Systems, vol. 25 (Curran Associates, Inc., Red Hook, NY), 3230–3238.Google Scholar
• Ferman B (2016) Reading the fine print: Information disclosure in the Brazilian credit card market. Management Sci. 62(12):3534–3548.Link, Google Scholar
• Gómez A, Prokopyev OA (2021) A mixed-integer fractional optimization approach to best subset selection. INFORMS J. Comput. 33(2):551–565.Abstract, Google Scholar
• Goodfellow I, Bengio Y, Courville A (2016) Deep Learning (MIT Press, Cambridge, MA).Google Scholar
• Han B, Ryzhov IO, Defourny B (2016) Optimal learning in linear regression with combinatorial feature selection. INFORMS J. Comput. 28(4):721–735.Link, Google Scholar
• Hazimeh H, Mazumder R (2020) Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms. Oper. Res. 68(5):1517–1537.Link, Google Scholar
• Hilscher J, Wilson M (2017) Credit ratings and credit risk: Is one measure enough? Management Sci. 63(10):3414–3437.Link, Google Scholar
• Hoffer E, Ailon N (2015) Deep metric learning using triplet network. Proc. Similarity-Based Pattern Recognition Third Internat. Workshop SIMBAD 2015 (Springer, Cham, Switzerland), 84–92.Google Scholar
• Hong LJ, Juneja S, Luo J (2014) Estimating sensitivities of portfolio credit risk using Monte Carlo. INFORMS J. Comput. 26(4):848–865.Link, Google Scholar
• Jiang H, Luo S, Dong Y (2021) Simultaneous feature selection and clustering based on square root optimization. Eur. J. Oper. Res. 289(1):214–231.Crossref, Google Scholar
• Kelley S, Ovchinnikov A, Hardoon DR, Heinrich A (2022) Antidiscrimination laws, artificial intelligence, and gender bias: A case study in nonmortgage fintech lending. Manufacturing Service Oper. Management 24(6):3039–3059.Link, Google Scholar
• Keshanian K, Zantedeschi D, Dutta K (2022) Features selection as a Nash-bargaining solution: Applications in online advertising and information systems. INFORMS J. Comput. 34(5):2485–2501.Link, Google Scholar
• Li T, Kou G, Peng Y, Yu PS (2021) A fast diagonal distance metric learning approach for large-scale datasets. Inform. Sci. 571:225–245.Crossref, Google Scholar
• Li T, Kou G, Peng Y, Yu PS (2024) Feature selection and grouping effect analysis for credit evaluation via regularized diagonal distance metric learning. http://dx.doi.org/10.1287/ijoc.2023.0322.cd, https://github.com/INFORMSJoC/2023.0322.Google Scholar
• Maldonado S, Bravo C, López J, Pérez J (2017) Integrated framework for profit-based feature selection and SVM classification in credit scoring. Decision Support Systems 104:113–121.Crossref, Google Scholar
• Meier L, Van De Geer S, Bühlmann P (2008) The group lasso for logistic regression. J. Roy. Statist. Soc. Ser. B Statist. Methodology 70(1):53–71.Crossref, Google Scholar
• Nguyen B, Morell C, De Baets B (2017) Supervised distance metric learning through maximization of the Jeffrey divergence. Pattern Recognition 64:215–225.Crossref, Google Scholar
• O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41:673–690.Crossref, Google Scholar
• Petersen A, Witten D, Simon N (2016) Fused lasso additive model. J. Comput. Graph. Statist. 25(4):1005–1025.Crossref, Google Scholar
• Piramuthu S (1999) Feature selection for financial credit-risk evaluation decisions. INFORMS J. Comput. 11(3):258–266.Link, Google Scholar
• Shi Y, Miao J, Wang Z, Zhang P, Niu L (2018) Feature selection with l2,1−2 regularization. IEEE Trans. Neural Networks Learn. Systems 29(10):4967–4982.Crossref, Google Scholar
• Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B Statist. Methodology 58(1):267–288.Crossref, Google Scholar
• Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K (2005) Sparsity and smoothness via the fused lasso. J. Roy. Statist. Soc. Ser. B Statist. Methodology 67(1):91–108.Crossref, Google Scholar
• Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J. Machine Learn. Res. 9(86):2579–2605.Google Scholar
• Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J. Machine Learn. Res. 10(9):207–244.Google Scholar
• Won D, Manzour H, Chaovalitwongse W (2020) Convex optimization for group feature selection in networked data. INFORMS J. Comput. 32(1):182–198.Link, Google Scholar
• Xiao J, Tian Y, Jia Y, Jiang X, Yu L, Wang S (2023) Black-box attack-based security evaluation framework for credit card fraud detection models. INFORMS J. Comput. 35(5):986–1001.Link, Google Scholar
• Xing E, Jordan M, Russell SJ, Ng A (2002) Distance metric learning with application to clustering with side-information. Proc. 15th Internat. Conf. Neural Inform. Processing Systems, vol. 15 (MIT Press, Cambridge, MA), 521–528.Google Scholar
• Ying Y, Li P (2012) Distance metric learning with eigenvalue optimization. J. Machine Learn. Res. 13(1):1–26.Google Scholar
• Yoganarasimhan H (2020) Search personalization using machine learning. Management Sci. 66(3):1045–1070.Link, Google Scholar
• Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J. Roy. Statist. Soc. Ser. B Statist. Methodology 68(1):49–67.Crossref, Google Scholar
• Zhang J, Wang C, Chen G (2021) A review selection method for finding an informative subset from online reviews. INFORMS J. Comput. 33(1):280–299.Link, Google Scholar
• Zheng Z, Zhang J, Li Y (2022) L0-regularized learning for high-dimensional additive hazards regression. INFORMS J. Comput. 34(5):2762–2775.Link, Google Scholar
• Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J. Roy. Statist. Soc. Ser. B Statist. Methodology 67(2):301–320.Crossref, Google Scholar