Neural network classifers in arrears management

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3697 LNCS, Issue , 2005, Pages 325-330

  Scheurmann, Esther ^a   Matthews, Chris ^a  

^a LA TROBE UNIVERSITY   (Australia)

Author keywords

Arrears management; Loan default; Majority under sampling; Minority over sampling; Neural network ensembles

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER SCIENCE; SIGNAL FILTERING AND PREDICTION;

ARREARS MANAGEMENT; LOAN DEFAULT; MAJORITY UNDER-SAMPLING; MINORITY OVER-SAMPLING;

NEURAL NETWORKS;

EID: 33646262889     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (17)

1. M.H.L.B. Abdullah and V. Ganapathy. Neural Network Ensemble for Financial Trend Prediction. In Proceedings TENCON 2000, volume 3, pages 157-161, Kuala Lumpar, Malaysia, 2000.
2. Bart Baesens, Tony Van Gestel, Maria Stepanova, and Jan Vanthienen. Neural Network Survival Analysis for Personal Loan Data. In Proceedings of the Eighth Conference on Credit Scoring and Credit Control (CSCC VIII'2003), Edinburgh, Scotland, 2003.
3. Nitesh V. Chawla, Lawrence O. Hall, Kevin Bowyer, and W. Philip Kegelmeyer. Learning Ensembles from Bites: A Scalable and Accurate Approach. Journal of Machine Learning, 5:421-451, 2004.
4. V.S. Desai, J.N. Crook, and G.A. Overstreet Jr. A Comparison of Neural Networks and Linear Scoring Models in the credit union environment. European Journal of Operational Research, 95:24-37, 1995.
5. Thomas G. Dietterich. Machine-Learning Research: Four Current Directions. AI Magazine, 18(4):97-136, 1997.
6. Y. Freund and R. Schapire. Experiments with a new boosting algorithm. In Proceedings Thirteenth International Conference on Machine Learning, Bari, Italy, 1996.
7. Hongyu Guo and Herna L. Viktor. Learning from Imbalanced Data Sets with Boosting and Data Generation: The Databoost-IM Approach. ACM SIGKDD Explorations Newsletter: Special Issue on Learning from Imbalanced Datasets, 6(1):30-39, June 2004.
8. L.K. Hansen and P. Salamon. Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10):993-1001, 1990.
9. E.M Lewis. An Introduction to Credit Scoring. The Athena Press, San Rafael, California, 1992.
10. Y. Liu, X. Yao, Q. Zhao, and T. Higuchi. An Experimental Comparison of Ensemble Learning Methods on Decision Boundaries. In Proceedings 2002 International Joint Conference on Neural Networks, IJCNN'02, volume 1, pages 221-226, Honolulu, HI USA, 2002.
11. E Mays. Handbook of Credit Scoring. Glenlake Publishing, Chicago, 2001.
12. Yair Shimshoni and Nathan Intrator. Classification of Seismic Signals by Integrating Ensembles of Neural Networks. IEEE Transactions on Signal Processing, 46(5):1194-1201, 1998.
13. R.P. Srivastva. Automating judgemental decisions using neural network: a model for processing business loan applications. In Proceedings of the 1992 ACM Conference on Communications, Kansas City, Missouri, 1992.
14. Nayer M. Wanas and Mohamed S. Kamel. Decision Fusion in Neural Network Ensembles. In Proceedings 2001 International Joint Conference on Neural Networks, IJCNN'01, pages 2952-2957, Washington, DC USA, 2001.
15. D. West. Neural Network Credit Scoring Models. Computer & Operations Research, 27:1131-1152, 2000.
16. Yunfeng Wu and Juan I. Arribas. Fusing Output Information in Neural Networks: Ensemble Performs Better. In Proceedings of the 25th Annual Conference of the IEEE EMBS, pages 2265-2268, Cancum, Mexico, 2003.
17. Xin Yao, Manfred Fischer, and Gavin Brown. Neural Network Ensembles and Their Application to Traffic Flow Prediction in Telecommunications Networks. In Proceedings 2001 International Joint Conference on Neural Networks, IJCNN'01, pages 693-698, Washington, DC USA, 2001.