Bio-Inspired Credit Risk Analysis
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Kin Keung Lai | Lean Yu | Shouyang Wang | Shouyang Wang | Ligang Zhou | K. Lai | Shouyang Wang | Lean Yu | Ligang Zhou
[1] Kin Keung Lai,et al. Credit risk assessment with a multistage neural network ensemble learning approach , 2008, Expert Syst. Appl..
[2] Bart Baesens,et al. Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines , 2007, Eur. J. Oper. Res..
[3] Mu-Chen Chen,et al. Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..
[4] Minghui Jiang,et al. Construction and Application of PSO-SVM Model for Personal Credit Scoring , 2007, International Conference on Computational Science.
[5] Tom Fawcett,et al. ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .
[6] Qi Fei,et al. A comparative study of data mining methods in consumer loans credit scoring management , 2006 .
[7] Kin Keung Lai,et al. Multistage Neural Network Metalearning with Application to Foreign Exchange Rates Forecasting , 2006, MICAI.
[8] Kin Keung Lai,et al. A Reliability-Based RBF Network Ensemble Model for Foreign Exchange Rates Predication , 2006, ICONIP.
[9] Liang Gao,et al. Credit Scoring Model Based on Neural Network with Particle Swarm Optimization , 2006, ICNC.
[10] Hewijin Christine Jiau,et al. Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem , 2006 .
[11] Chenguang Yang,et al. Credit Risk Assessment in Commercial Banks Based on Multi-layer SVM Classifier , 2006, ICIC.
[12] Kin Keung Lai,et al. Neural Network Metalearning for Credit Scoring , 2006, ICIC.
[13] Kin Keung Lai,et al. Credit Risk Evaluation with Least Square Support Vector Machine , 2006, RSKT.
[14] Lean Yu,et al. A New Method for Crude Oil Price Forecasting Based on Support Vector Machines , 2006, International Conference on Computational Science.
[15] Kin Keung Lai,et al. A Bias-Variance-Complexity Trade-Off Framework for Complex System Modeling , 2006, ICCSA.
[16] Sheng-Tun Li,et al. The evaluation of consumer loans using support vector machines , 2006, Expert Syst. Appl..
[17] Jih-Jeng Huang,et al. Two-stage genetic programming (2SGP) for the credit scoring model , 2006, Appl. Math. Comput..
[18] Kin Keung Lai,et al. An integrated data preparation scheme for neural network data analysis , 2006, IEEE Transactions on Knowledge and Data Engineering.
[19] Ralf Stecking,et al. Selecting SVM Kernels and Input Variable Subsets in Credit Scoring Models , 2006, GfKl.
[20] Ralf Stecking,et al. Combining Support Vector Machines for Credit Scoring , 2006, OR.
[21] Kin Keung Lai,et al. A new fuzzy support vector machine to evaluate credit risk , 2005, IEEE Transactions on Fuzzy Systems.
[22] Kin Keung Lai,et al. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates , 2005, Comput. Oper. Res..
[23] Bart Baesens,et al. A Comprehensible SOM-Based Scoring System , 2005, MLDM.
[24] David J. Hand,et al. A survey of the issues in consumer credit modelling research , 2005, J. Oper. Res. Soc..
[25] Malcolm J. Beynon,et al. A method of aggregation in DS/AHP for group decision-making with the non-equivalent importance of individuals in the group , 2005, Comput. Oper. Res..
[26] C ONG,et al. Building credit scoring models using genetic programming , 2005, Expert Syst. Appl..
[27] Ralf Stecking,et al. Support vector machines for classifying and describing credit applicants: detecting typical and critical regions , 2005, J. Oper. Res. Soc..
[28] Bart Baesens,et al. Neural network survival analysis for personal loan data , 2005, J. Oper. Res. Soc..
[29] Tian-Shyug Lee,et al. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines , 2005, Expert Syst. Appl..
[30] Bogdan Gabrys,et al. Classifier selection for majority voting , 2005, Inf. Fusion.
[31] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[32] Yigui Ou,et al. A SUPERLINEARLY CONVERGENT TRUST REGION ALGORITHM FOR LC 1 CONSTRAINED OPTIMIZATION PROBLEMS , 2005 .
[33] Ralf Stecking,et al. Support Vector Machines for Credit Scoring: Extension to Non Standard Cases , 2005 .
[34] Ralf Stecking,et al. Variable Subset Selection for Credit Scoring with Support Vector Machines , 2005, OR.
[35] Cheng-Lin Liu,et al. Classifier combination based on confidence transformation , 2005, Pattern Recognit..
[36] Kyung-shik Shin,et al. An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..
[37] Antony Browne,et al. Neural network ensembles: combining multiple models for enhanced performance using a multistage approach , 2004, Expert Syst. J. Knowl. Eng..
[38] Nan-Chen Hsieh,et al. An integrated data mining and behavioral scoring model for analyzing bank customers , 2004, Expert Syst. Appl..
[39] Renpu Li,et al. Mining classification rules using rough sets and neural networks , 2004, Eur. J. Oper. Res..
[40] Geoffrey I. Webb,et al. Multistrategy ensemble learning: reducing error by combining ensemble learning techniques , 2004, IEEE Transactions on Knowledge and Data Engineering.
[41] De-Shuang Huang,et al. Least squares support vector machine ensemble , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[42] Jianping Li,et al. Support Vector Machines Approach to Credit Assessment , 2004, International Conference on Computational Science.
[43] William Nick Street,et al. An intelligent system for customer targeting: a data mining approach , 2004, Decis. Support Syst..
[44] Chengyi Xiong,et al. Novel algorithm for image interpolation , 2004 .
[45] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[46] Ning Zhong,et al. Using Rough Sets with Heuristics for Feature Selection , 1999, Journal of Intelligent Information Systems.
[47] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.
[48] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[49] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[50] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[51] Bart Baesens,et al. Comprehensible Credit-Scoring Knowledge Visualization Using Decision Tables and Diagrams , 2004, ICEIS.
[52] C.A.M. Lima,et al. GA-based selection of components for heterogeneous ensembles of support vector machines , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[53] Hyun-Chul Kim,et al. Constructing support vector machine ensemble , 2003, Pattern Recognit..
[54] Jie Lu,et al. An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria , 2003 .
[55] Martin Schader,et al. Between Data Science and Applied Data Analysis , 2003 .
[56] Kyoung-jae Kim,et al. Financial time series forecasting using support vector machines , 2003, Neurocomputing.
[57] Susan E. Bedingfield,et al. Predicting Bad Credit Risk: An Evolutionary Approach , 2003, ICANN.
[58] Shigeo Abe,et al. Fuzzy least squares support vector machines for multiclass problems , 2003, Neural Networks.
[59] Mu-Chen Chen,et al. Credit scoring and rejected instances reassigning through evolutionary computation techniques , 2003, Expert Syst. Appl..
[60] Robert P. W. Duin,et al. Limits on the majority vote accuracy in classifier fusion , 2003, Pattern Analysis & Applications.
[61] Johan A. K. Suykens,et al. Bankruptcy prediction with least squares support vector machine classifiers , 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..
[62] Andrzej Skowron,et al. Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..
[63] Bart Baesens,et al. Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation , 2003, Manag. Sci..
[64] Ralf Stecking,et al. Support Vector Machines for Credit Scoring: Comparing to and Combining With Some Traditional Classification Methods , 2003 .
[65] B. Baesens,et al. A support vector machine approach to credit scoring , 2003 .
[66] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[67] Chih-Chou Chiu,et al. Credit scoring using the hybrid neural discriminant technique , 2002, Expert Syst. Appl..
[68] Johan A. K. Suykens,et al. Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.
[69] Guido Smits,et al. Improved SVM regression using mixtures of kernels , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).
[70] Ludmila I. Kuncheva,et al. Relationships between combination methods and measures of diversity in combining classifiers , 2002, Inf. Fusion.
[71] Ludmila I. Kuncheva,et al. Switching between selection and fusion in combining classifiers: an experiment , 2002, IEEE Trans. Syst. Man Cybern. Part B.
[72] Sheng-De Wang,et al. Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.
[73] Ludmila I. Kuncheva,et al. A Theoretical Study on Six Classifier Fusion Strategies , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[74] S. Uryasev,et al. Credit cards scoring with quadratic utility functions , 2002 .
[75] Rashmi Malhotra,et al. Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems , 2001, Eur. J. Oper. Res..
[76] Kagan Tumer,et al. Robust Combining of Disparate Classifiers through Order Statistics , 1999, Pattern Analysis & Applications.
[77] Nadine Meskens,et al. A comparison of rough sets and recursive partitioning induction approaches : an application to commercial loans , 2002 .
[78] Malcolm J. Beynon,et al. Reducts within the variable precision rough sets model: A further investigation , 2001, Eur. J. Oper. Res..
[79] F. Tay,et al. Application of support vector machines in financial time series forecasting , 2001 .
[80] R. Malhotra,et al. Evaluating Consumer Loans Using Neural Networks , 2001 .
[81] Lakhmi C. Jain,et al. Designing classifier fusion systems by genetic algorithms , 2000, IEEE Trans. Evol. Comput..
[82] David West,et al. Neural network credit scoring models , 2000, Comput. Oper. Res..
[83] James T. Kwok,et al. The evidence framework applied to support vector machines , 2000, IEEE Trans. Neural Networks Learn. Syst..
[84] Ching Y. Suen,et al. Multiple Classifier Combination Methodologies for Different Output Levels , 2000, Multiple Classifier Systems.
[85] J. Crook,et al. Credit scoring using neural and evolutionary techniques , 2000 .
[86] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[87] Byeong Seok Ahn,et al. The integrated methodology of rough set theory and artificial neural network for business failure prediction , 2000 .
[88] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[89] Mineichi Kudo,et al. Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..
[90] S. Sathiya Keerthi,et al. A fast iterative nearest point algorithm for support vector machine classifier design , 2000, IEEE Trans. Neural Networks Learn. Syst..
[91] Toshimitsu Ushio,et al. Rule induction from inconsistent and incomplete data using rough sets , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).
[92] Leo Breiman,et al. Prediction Games and Arcing Algorithms , 1999, Neural Computation.
[93] Xin Yao,et al. Evolving artificial neural networks , 1999, Proc. IEEE.
[94] Vladimir Vapnik,et al. An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.
[95] Xuegong Zhang,et al. Using class-center vectors to build support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).
[96] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[97] Selwyn Piramuthu,et al. Financial credit-risk evaluation with neural and neurofuzzy systems , 1999, Eur. J. Oper. Res..
[98] Thorsten Joachims,et al. Making large-scale support vector machine learning practical , 1999 .
[99] Franco Varetto. Genetic algorithms applications in the analysis of insolvency risk , 1998 .
[100] Albrecht Irion,et al. Fuzzy rules and fuzzy functions: A combination of logic and arithmetic operations for fuzzy numbers , 1998, Fuzzy Sets Syst..
[101] Nello Cristianini,et al. The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines , 1998, ICML.
[102] Francisco Herrera,et al. Combining Numerical and Linguistic Information in Group Decision Making , 1998, Inf. Sci..
[103] Jaap Spronk,et al. The Application of the Multi-Factor Model in the Analysis of Corporate Failure , 1998 .
[104] V. Vapnik. The Support Vector Method of Function Estimation , 1998 .
[105] Johan A. K. Suykens,et al. Nonlinear modeling : advanced black-box techniques , 1998 .
[106] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[107] Pedro M. Domingos,et al. How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications , 1998 .
[108] Kevin N. Gurney,et al. An introduction to neural networks , 2018 .
[109] William V. Gehrlein,et al. A two-stage least cost credit scoring model , 1997, Ann. Oper. Res..
[110] Jonathan Crook,et al. Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms , 1997 .
[111] David J. Hand,et al. Construction of a k-nearest-neighbour credit-scoring system , 1997 .
[112] Huan Liu,et al. Feature Selection for Classification , 1997, Intell. Data Anal..
[113] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[114] G. Terrell,et al. Iterated grid search algorithm on unimodal criteria , 1997 .
[115] Josef Kittler,et al. Combining classifiers , 1996, Proceedings of 13th International Conference on Pattern Recognition.
[116] Constantin Zopounidis,et al. A survey of business failures with an emphasis on prediction methods and industrial applications , 1996 .
[117] D. Hand,et al. A k-nearest-neighbour classifier for assessing consumer credit risk , 1996 .
[118] Krzysztof J. Cios,et al. Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model , 1996, Neurocomputing.
[119] Soung Hie Kim,et al. A note on the fuzzy weighted additive rule , 1996, Fuzzy Sets Syst..
[120] Ilona Jagielska,et al. Neural network for predicting the performance of credit card accounts , 1996 .
[121] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[122] Joachim Diederich,et al. Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..
[123] Wojciech Ziarko,et al. INTRODUCTION TO THE SPECIAL ISSUE ON ROUGH SETS AND KNOWLEDGE DISCOVERY , 1995, Comput. Intell..
[124] R. Yager. Aggregation operators and fuzzy systems modeling , 1994 .
[125] Andrzej Skowron,et al. Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables , 1994, ISMIS.
[126] Constantin Zopounidis,et al. An integrated DSS for financing firms by an industrial development bank in Greece , 1994, Decis. Support Syst..
[127] Eric Rosenberg,et al. Quantitative Methods in Credit Management: A Survey , 1994, Oper. Res..
[128] Cullen Schaffer,et al. A Conservation Law for Generalization Performance , 1994, ICML.
[129] Jude W. Shavlik,et al. Using Sampling and Queries to Extract Rules from Trained Neural Networks , 1994, ICML.
[130] Michael Conrad,et al. Combining evolution with credit apportionment: A new learning algorithm for neural nets , 1994, Neural Networks.
[131] Robert J. Plemmons,et al. Nonnegative Matrices in the Mathematical Sciences , 1979, Classics in Applied Mathematics.
[132] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.
[133] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[134] Herbert L. Jensen,et al. Using Neural Networks for Credit Scoring , 1992 .
[135] Adam Krzyżak,et al. Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..
[136] R. Ramakrishnan,et al. The fuzzy weighted additive rule , 1992 .
[137] Chris Bishop,et al. Improving the Generalization Properties of Radial Basis Function Neural Networks , 1991, Neural Computation.
[138] Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .
[139] K. Keasey,et al. Financial Distress Prediction Models: A Review of Their Usefulness1 , 1991 .
[140] D. Fogel. System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .
[141] Jooyoung Park,et al. Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.
[142] Erkki K. Laitinen,et al. Survival analysis as a tool for company failure prediction , 1991 .
[143] Sheng Chen,et al. Non-linear systems identification using radial basis functions , 1990 .
[144] Fred Glover,et al. IMPROVED LINEAR PROGRAMMING MODELS FOR DISCRIMINANT ANALYSIS , 1990 .
[145] Halbert White,et al. Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.
[146] Lars Kai Hansen,et al. Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[147] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[148] A. Steenackers,et al. A credit scoring model for personal loans , 1989 .
[149] R. Fletcher. Practical Methods of Optimization , 1988 .
[150] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[151] Chris Carter,et al. Assessing Credit Card Applications Using Machine Learning , 1987, IEEE Expert.
[152] Gerardine DeSanctis,et al. A foundation for the study of group decision support systems , 1987 .
[153] F Jones,et al. CURRENT TECHNIQUES IN BANKRUPTCY PREDICTION , 1987 .
[154] I. Jolliffe. Principal Component Analysis , 2005 .
[155] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[156] W. Cholewa. Aggregation of fuzzy opinions — an axiomatic approach , 1985 .
[157] H. Frydman,et al. Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress , 1985 .
[158] G. Bortolan,et al. A review of some methods for ranking fuzzy subsets , 1985 .
[159] M. Zmijewski. METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .
[160] C. Zavgren,et al. The prediction of corporate failure: The state of the art , 1983 .
[161] David J. Hand,et al. Discrimination and Classification , 1982 .
[162] J. Wiginton. A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior , 1980, Journal of Financial and Quantitative Analysis.
[163] Donald R. Cooper,et al. Business Research Methods , 1980 .
[164] Rolph E. Anderson,et al. Multivariate Data Analysis with Readings , 1979 .
[165] V. N. Malozemov,et al. Finding the Point of a Polyhedron Closest to the Origin , 1974 .
[166] F. Black,et al. The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.
[167] Michael D. Geurts,et al. Time Series Analysis: Forecasting and Control , 1977 .
[168] Samprit Chatterjee,et al. A Nonparametric Approach to Credit Screening , 1970 .
[169] Edward I. Altman,et al. FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .
[170] W. Beaver. Financial Ratios As Predictors Of Failure , 1966 .
[171] O. Mangasarian. Linear and Nonlinear Separation of Patterns by Linear Programming , 1965 .
[172] James H. Myers,et al. The Development of Numerical Credit Evaluation Systems , 1963 .
[173] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .