Combining B&B-based hybrid feature selection and the imbalance-oriented multiple-classifier ensemble for imbalanced credit risk assessment
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Jie Sun | Young-Chan Lee | Hui Li | Qing-Hua Huang | Hui Li | Jie Sun | Young-Chan Lee | Qinghua Huang
[1] Chen Ying,et al. The comparison of enterprise bankruptcy forecasting method , 2011 .
[2] M. Chijoriga,et al. Application of multiple discriminant analysis (MDA) as a credit scoring and risk assessment model , 2011 .
[3] Jianping Li,et al. A weighted Lq adaptive least squares support vector machine classifiers - Robust and sparse approximation , 2011, Expert Syst. Appl..
[4] Kin Keung Lai,et al. Credit scorecard based on logistic regression with random coefficients , 2010, ICCS.
[5] Rūta Adlytė,et al. New internal rating approach for credit risk assessment , 2011 .
[6] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[7] Kin Keung Lai,et al. An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring , 2009, Eur. J. Oper. Res..
[8] Anjan V. Thakor,et al. Collateral and Rationing: Sorting Equilibria in Monopolistic and Competitive Credit Markets , 1987 .
[9] Xia Han. The Model of Credit Risk Assessment in Commercial Banks on Fuzzy Integral Support Vector Machines Ensemble , 2009 .
[10] Rong Yan,et al. On predicting rare classes with SVM ensembles in scene classification , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[11] Robert A. Eisenbeis,et al. PITFALLS IN THE APPLICATION OF DISCRIMINANT ANALYSIS IN BUSINESS, FINANCE, AND ECONOMICS , 1977 .
[12] E. Laitinen. Predicting a corporate credit analyst's risk estimate by logistic and linear models , 1999 .
[13] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[14] M. Maloof. Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown , 2003 .
[15] Yong Shi,et al. Credit risk evaluation by using nearest subspace method , 2010, ICCS.
[16] Soushan Wu,et al. Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..
[17] J. Stiglitz,et al. Credit Rationing in Markets with Imperfect Information , 1981 .
[18] Chih-Jen Lin,et al. Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..
[19] Jonathan N. Crook,et al. Recent developments in consumer credit risk assessment , 2007, Eur. J. Oper. Res..
[20] Kin Keung Lai,et al. Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection , 2011, Expert Syst. Appl..
[21] Smaranda Stoenescu Cimpoeru,et al. Neural networks and their application in credit risk assessment. Evidence from the Romanian market , 2011 .
[22] Wen Yi-min,et al. A survey of imbalanced pattern classification problems , 2009 .
[23] Jian Ma,et al. A comparative assessment of ensemble learning for credit scoring , 2011, Expert Syst. Appl..
[24] Aristidis Likas,et al. Semi-supervised and active learning with the probabilistic RBF classifier , 2008, Neurocomputing.
[25] James J. Chen,et al. Classification by ensembles from random partitions of high-dimensional data , 2007, Comput. Stat. Data Anal..
[26] Gianluca Antonini,et al. Subagging for credit scoring models , 2010, Eur. J. Oper. Res..
[27] Helmut Bester,et al. Screening vs. Rationing in Credit Markets with Imperfect Information , 1985 .
[28] Po-Cheng Chen,et al. An enforced support vector machine model for construction contractor default prediction , 2011 .
[29] S. M. Finlay. Towards profitability: a utility approach to the credit scoring problem , 2008, J. Oper. Res. Soc..
[30] T Bellotti,et al. Credit scoring with macroeconomic variables using survival analysis , 2009, J. Oper. Res. Soc..
[31] Kenneth Kennedy,et al. Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem , 2009, AICS.
[32] Nitesh V. Chawla,et al. Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.
[33] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[34] E. L. Lawler,et al. Branch-and-Bound Methods: A Survey , 1966, Oper. Res..
[35] Hui Li,et al. Dynamic financial distress prediction using instance selection for the disposal of concept drift , 2011, Expert Syst. Appl..
[36] Stephen D. Williamson. Costly monitoring, financial intermediation, and equilibrium credit rationing , 1986 .
[37] Bart Baesens,et al. Credit rating prediction using Ant Colony Optimization , 2010, J. Oper. Res. Soc..
[38] Raquel Florez-Lopez,et al. Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data , 2010 .
[39] Arijit Laha. Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring , 2007, Adv. Eng. Informatics.
[40] Ying Wah Teh,et al. Credit Scoring Models Using Soft Computing Methods: A Survey , 2010, Int. Arab J. Inf. Technol..
[41] M. Ibrahímo,et al. Asymmetric Information and Models of Credit Rationing , 1993 .
[42] Lin Ma,et al. Mining the customer credit using hybrid support vector machine technique , 2009, Expert Syst. Appl..
[43] Chris Stewart,et al. A note comparing support vector machines and ordered choice models' predictions of international banks' ratings , 2011, Decis. Support Syst..
[44] Bart Baesens,et al. Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines , 2007, Eur. J. Oper. Res..
[45] Kin Keung Lai,et al. Credit risk assessment with a multistage neural network ensemble learning approach , 2008, Expert Syst. Appl..
[46] Wuyi Yue,et al. Support vector machine based multiagent ensemble learning for credit risk evaluation , 2010, Expert Syst. Appl..
[47] C. Spearman. General intelligence Objectively Determined and Measured , 1904 .
[48] Xue-wen Chen. An improved branch and bound algorithm for feature selection , 2003, Pattern Recognit. Lett..
[49] Christophe Mues,et al. An experimental comparison of classification algorithms for imbalanced credit scoring data sets , 2012, Expert Syst. Appl..
[50] Kin Keung Lai,et al. Least squares support vector machines ensemble models for credit scoring , 2010, Expert Syst. Appl..
[51] Shian-Chang Huang,et al. Integrating nonlinear graph based dimensionality reduction schemes with SVMs for credit rating forecasting , 2009, Expert Syst. Appl..
[52] Andrea Roli,et al. A neural network approach for credit risk evaluation , 2008 .
[53] Huan Liu,et al. Feature Selection for Classification , 1997, Intell. Data Anal..
[54] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[55] Xiang Yu,et al. Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies , 2011 .
[56] Chih-Fong Tsai,et al. Using neural network ensembles for bankruptcy prediction and credit scoring , 2008, Expert Syst. Appl..
[57] José Salvador Sánchez,et al. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance , 2012, Knowl. Based Syst..
[58] Bart Baesens,et al. Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation , 2003, Manag. Sci..
[59] Jie Sun,et al. An Application of Support Vector Machine to Companies' Financial Distress Prediction , 2006, MDAI.
[60] Stan Matwin,et al. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.
[61] Angie Wade,et al. When t-tests or Wilcoxon-Mann-Whitney tests won't do. , 2010, Advances in physiology education.
[62] Adnan Khashman,et al. Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes , 2010, Expert Syst. Appl..
[63] David West,et al. Neural network ensemble strategies for financial decision applications , 2005, Comput. Oper. Res..
[64] Chih-Fong Tsai,et al. Feature selection in bankruptcy prediction , 2009, Knowl. Based Syst..
[65] Jonathan Crook,et al. Support vector machines for credit scoring and discovery of significant features , 2009, Expert Syst. Appl..