Mathematical programming models for classification problems with applications to credit scoring
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[1] Fred Glover,et al. LINEAR PROGRAMMING AND STATISTICAL DISCRIMINATION THE LP SIDE , 1982 .
[2] Neil Salkind. Encyclopedia of Measurement and Statistics , 2006 .
[3] Terence C. Fogarty,et al. Evolving Bayesian classifiers for credit control—a comparison with other machine-learning methods , 1993 .
[4] Guido Dedene,et al. A Comparison of State-of-The-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection , 2002 .
[5] H. D. Brunk,et al. Statistical inference under order restrictions : the theory and application of isotonic regression , 1973 .
[6] V. Srinivasan,et al. CGX: An expert support system for credit granting , 1990 .
[7] Jonathan Crook,et al. Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms , 1997 .
[8] Bill C. Hardgrave,et al. An improved method for developing neural networks: The case of evaluating commercial loan creditworthiness , 1996, Comput. Oper. Res..
[9] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[10] Edward P. Markowski,et al. SOME DIFFICULTIES AND IMPROVEMENTS IN APPLYING LINEAR PROGRAMMING FORMULATIONS TO THE DISCRIMINANT PROBLEM , 1985 .
[11] F. Deng,et al. A credit scoring model using Support Vector Machine , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).
[12] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[13] Constantin Zopounidis,et al. FINCLAS: A MULTICRITERIA DECISION SUPPORT SYSTEM FOR FINANCIAL CLASSIFICATION PROBLEMS , 1998 .
[14] Prakash L. Abad,et al. On the performance of linear programming heuristics applied on a quadratic transformation in the classification problem , 1994 .
[15] Y. Liu,et al. Data mining feature selection for credit scoring models , 2005, J. Oper. Res. Soc..
[16] Edward I. Altman,et al. FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .
[17] Brian D. Ripley,et al. Neural Networks and Related Methods for Classification , 1994 .
[18] Marijana Zekić-Sušac,et al. Modelling small-business credit scoring by using logistic regression, neural networks and decision trees , 2005, Intell. Syst. Account. Finance Manag..
[19] Ehsan Nikbakht,et al. APPLICATION OF EXPERT SYSTEMS IN EVALUATION OF CREDIT CARD BORROWERS , 1989 .
[20] N. Capon. Credit Scoring Systems: A Critical Analysis , 1982 .
[21] Paul S. Bradley,et al. Feature Selection via Mathematical Programming , 1997, INFORMS J. Comput..
[22] L. Thomas. A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers , 2000 .
[23] Shen Zheng,et al. An Algorithm for Score Calibration Based on Cumulative Bad Rates , 2003, Int. J. Inf. Technol. Decis. Mak..
[24] Panos M. Pardalos,et al. Fuzzy Sets in Management, Economics and Marketing , 2001 .
[25] William V. Gehrlein,et al. A two-stage least cost credit scoring model , 1997, Ann. Oper. Res..
[26] Roger M. Stein,et al. Validation methodologies for default risk models , 2022 .
[27] Alan K. Reichert,et al. An Examination of the Conceptual Issues Involved in Developing Credit-Scoring Models , 1983 .
[28] Yannis Siskos,et al. Preference disaggregation: 20 years of MCDA experience , 2001, Eur. J. Oper. Res..
[29] John J. Glen,et al. A comparison of standard and two-stage mathematical programming discriminant analysis methods , 2006, Eur. J. Oper. Res..
[30] Fred W. Glover,et al. A Template for Scatter Search and Path Relinking , 1997, Artificial Evolution.
[31] Kim Fung Lam,et al. An experimental comparison of some recently developed linear programming approaches to the discriminant problem , 1997, Comput. Oper. Res..
[32] Canada. Linear Goal Programming in Estimation of Classification Probability , 1989 .
[33] S. Uryasev,et al. Credit cards scoring with quadratic utility functions , 2002 .
[34] Toshiyuki Sueyoshi,et al. DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches , 2006, Eur. J. Oper. Res..
[35] Yair E. Orgler. Analytical methods in loan evaluation , 1975 .
[36] C. Zopounidis,et al. A Multicriteria Decision Aid Methodology for Sorting Decision Problems: The Case of Financial Distress , 1999 .
[37] Antonie Stam,et al. A comparison of a robust mixed-integer approach to existing methods for establishing classification rules for the discriminant problem , 1990 .
[38] David J. Hand,et al. Choosing k for two-class nearest neighbour classifiers with unbalanced classes , 2003, Pattern Recognit. Lett..
[39] Panos M. Pardalos,et al. Multicriteria Decision Aid in Credit Cards Assessment , 1998 .
[40] Kostas S. Metaxiotis,et al. Expert systems in business: applications and future directions for the operations researcher , 2003, Ind. Manag. Data Syst..
[41] Christophe Mues,et al. An experimental comparison of classification algorithms for imbalanced credit scoring data sets , 2012, Expert Syst. Appl..
[42] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[43] Martha Poon,et al. Scorecards as Devices for Consumer Credit: The Case of Fair, Isaac & Company Incorporated , 2007 .
[44] Paul A. Rubin,et al. Solving mixed integer classification problems by decomposition , 1997, Ann. Oper. Res..
[45] Johannes Grotendorst,et al. Classification of Highly Unbalanced CYP450 Data of Drugs Using Cost Sensitive Machine Learning Techniques. , 2007 .
[46] David J. Hand,et al. Statistical fraud detection: A review , 2002 .
[47] Robert J. Pavur,et al. Experimental evaluation of the classificatory performance of mathematical programming approaches to the three-group discriminant problem: The case of small samples , 1997, Ann. Oper. Res..
[48] John Glen,et al. Classification accuracy in discriminant analysis: a mixed integer programming approach , 2001, J. Oper. Res. Soc..
[49] Marko Robnik-Sikonja,et al. Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.
[50] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[51] Robert J. Pavur,et al. Examination of the classificatory performance of MIP models with secondary goals for the two-group discriminant problem , 1997, Ann. Oper. Res..
[52] John J. Glen,et al. An iterative mixed integer programming method for classification accuracy maximizing discriminant analysis , 2003, Comput. Oper. Res..
[53] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[54] John M. Liittschwager,et al. Integer Programming Solution of a Classification Problem , 1978 .
[55] Daniel Enache,et al. Analyzing Credit Risk Data: A Comparison of Logistic Discrimination, Classification Tree Analysis, a , 1997 .
[56] Sankar K. Pal,et al. Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing , 2004 .
[57] J. Freidman,et al. Multivariate adaptive regression splines , 1991 .
[58] Thomas G. Dietterich. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.
[59] A. Stam,et al. Classification performance of mathematical programming techniques in discriminant analysis: Results for small and medium sample sizes , 1990 .
[60] Jian Ma,et al. Rough set and scatter search metaheuristic based feature selection for credit scoring , 2012, Expert Syst. Appl..
[61] Abraham Charnes,et al. Optimal Estimation of Executive Compensation by Linear Programming , 1955 .
[62] Constantin Zopounidis,et al. Multicriteria classification and sorting methods: A literature review , 2002, Eur. J. Oper. Res..
[63] Cliff T. Ragsdale,et al. On the classification gap in mathematical programming-based approaches to the discriminant problem , 1992 .
[64] Derrick N. Joanes,et al. Reject inference applied to logistic regression for credit scoring , 1993 .
[65] Bart Baesens,et al. Filter‐ versus wrapper‐based feature selection for credit scoring , 2005, Int. J. Intell. Syst..
[66] Mu-Chen Chen,et al. Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..
[67] Pradit Wanarat,et al. Examining the effect of second-order terms in mathematical programming approaches to the classification problem , 1996 .
[68] Robert A. Eisenbeis,et al. PITFALLS IN THE APPLICATION OF DISCRIMINANT ANALYSIS IN BUSINESS, FINANCE, AND ECONOMICS , 1977 .
[69] E. Laitinen. Predicting a corporate credit analyst's risk estimate by logistic and linear models , 1999 .
[70] Yannis Siskos,et al. Multicriteria job evaluation for large organizations , 2001, Eur. J. Oper. Res..
[71] Galina Andreeva,et al. Credit risk in the context of European integration: assessing the possibility of Pan-European scoring , 2004 .
[72] Carol A. Markowski. On the balancing of error rates for LP discriminant methods , 1990 .
[73] Lucila Ohno-Machado,et al. Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.
[74] Feng-Chia Li,et al. Combination of feature selection approaches with SVM in credit scoring , 2010, Expert Syst. Appl..
[75] Jake Ansell,et al. Predicting default of a small business using different definitions of financial distress , 2012, J. Oper. Res. Soc..
[76] Prakash L. Abad,et al. New LP based heuristics for the classification problem , 1993 .
[77] Ned Freed,et al. EVALUATING ALTERNATIVE LINEAR PROGRAMMING MODELS TO SOLVE THE TWO-GROUP DISCRIMINANT PROBLEM , 1986 .
[78] Sylvia Lane,et al. Submarginal Credit Risk Classification , 1972 .
[79] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[80] Francisco Louzada,et al. On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data , 2012, Expert Syst. Appl..
[81] Michel Beuthe,et al. Comparative analysis of UTA multicriteria methods , 2001, Eur. J. Oper. Res..
[82] J. Cramer,et al. Scoring Bank Loans that may go wrong – A Case Study , 2000 .
[83] J. C. Dunn,et al. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .
[84] David J. Hand,et al. Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .
[85] Soner Akkoç,et al. An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data , 2012, Eur. J. Oper. Res..
[86] Ping Yao,et al. Neighborhood rough set and SVM based hybrid credit scoring classifier , 2011, Expert Syst. Appl..
[87] Toshiyuki Sueyoshi,et al. DEA-discriminant analysis in the view of goal programming , 1999, Eur. J. Oper. Res..
[88] Songbo Tan,et al. Neighbor-weighted K-nearest neighbor for unbalanced text corpus , 2005, Expert Syst. Appl..
[89] Antanas Verikas,et al. Feature selection with neural networks , 2002, Pattern Recognit. Lett..
[90] J. Anderson,et al. Constrained Discrimination between K Populations , 1969 .
[91] Richard C. Larson,et al. Model Building in Mathematical Programming , 1979 .
[92] R. O. Edmister,et al. JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS March 1972 AN EMPIRICAL TEST OF FINANCIAL RATIO ANALYSIS FOR SMALL BUSINESS FAILURE PREDICTION , 2009 .
[93] Konstantinos Falangis,et al. Heuristics for feature selection in mathematical programming discriminant analysis models , 2010, J. Oper. Res. Soc..
[94] Konstantinos Falangis,et al. The use of MSD model in credit scoring , 2007, Oper. Res..
[95] Chih-Fong Tsai,et al. Feature selection in bankruptcy prediction , 2009, Knowl. Based Syst..