- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Advances in Artificial Intelligence
Volume 2011 (2011), Article ID 587285, 12 pages
Tuning Expert Systems for Cost-Sensitive Decisions
Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201-0742, USA
Received 15 December 2010; Accepted 22 March 2011
Academic Editor: Filip Zelezny
Copyright © 2011 Atish P. Sinha and Huimin Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- D. A. Waterman, A Guide to Expert Systems, Addison-Wesley, Reading, Mass, USA, 1986.
- C. W. Holsapple and A. B. Whinston, Business Expert Systems, Irwin, Homewood, Ill, USA, 1987.
- R. J. Mockler and D. G. Dologite, Knowledge-Based Systems: An Introduction to Expert Systems, Macmillan, New York, NY, USA, 1992.
- E. Turban, Decision Support and Expert Systems: Management Support Systems, Macmillan, New York, NY, USA, 3rd edition, 1993.
- H. Zhao, “A multi-objective genetic programming approach to developing Pareto optimal decision trees,” Decision Support Systems, vol. 43, no. 3, pp. 809–826, 2007.
- H. Zhao, “Instance weighting versus threshold adjusting for cost-sensitive classification,” Knowledge and Information Systems, vol. 15, no. 3, pp. 321–334, 2008.
- H. Zhao, A. P. Sinha, and W. Ge, “Effects of feature construction on classification performance: an empirical study in bank failure prediction,” Expert Systems with Applications, vol. 36, no. 2, pp. 2633–2644, 2009.
- A. P. Sinha and J. H. May, “Evaluating and tuning predictive data mining models using receiver operating characteristic curves,” Journal of Management Information Systems, vol. 21, no. 3, pp. 249–280, 2004.
- A. P. Sinha and H. Zhao, “Incorporating domain knowledge into data mining classifiers: an application in indirect lending,” Decision Support Systems, vol. 46, no. 1, pp. 287–299, 2008.
- R. Dybowski, K. B. Laskey, J. W. Myers, and S. Parsons, “Introduction to the special issue on the fusion of domain knowledge with data for decision support,” Journal of Machine Learning Research, vol. 4, no. 3, pp. 293–294, 2004.
- I. Kopanas, N. M. Avouris, and S. Daskalaki, “The role of domain knowledge in a large scale data mining project,” in Applications of Artificial Intelligence, I. P. Vlahavas and C. D. Spyropoulos, Eds., Lecture Notes in AI, no. 2308, pp. 288–299, Springer, Berlin, Germany, 2002.
- S. Muggleton, Inductive Acquisition of Expert Knowledge, Addison-Wesley, Reading, Mass, USA, 1990.
- P. E. Johnson, “What kind of expert should a system be?” Journal of Medicine and Philosophy, vol. 8, no. 1, pp. 77–97, 1983.
- R. M. O'Keefe, O. Balci, and E. P. Smith, “Validating expert system performance,” IEEE Expert, vol. 2, no. 4, pp. 81–90, 1988.
- S. Lee and R. M. O'Keefe, “Developing a strategy for expert system verification and validation,” IEEE Transactions on Systems, Man and Cybernetics, vol. 24, no. 4, pp. 643–655, 1994.
- D. E. O’Leary, “Validation of expert systems—with applications to auditing and accounting expert systems,” Decision Sciences, vol. 18, no. 3, pp. 468–486, 1987.
- D. E. O'Leary, “Verification of uncertain knowledge-based systems: an empirical verification approach,” Management Science, vol. 42, no. 12, pp. 1663–1675, 1996.
- C. Elkan, “The foundations of cost-sensitive learning,” in Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 973–978, Seattle, Wash, USA, 2001.
- G. Bansal, A. P. Sinha, and H. Zhao, “Tuning data mining methods for cost-sensitive regression: a study in loan charge-off forecasting,” Journal of Management Information Systems, vol. 25, no. 3, pp. 315–336, 2009.
- A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern Recognition, vol. 30, no. 7, pp. 1145–1159, 1997.
- F. Provost, T. Fawcett, and R. Kohavi, “The case against accuracy estimation for comparing induction algorithms,” in Proceedings of the 15th International Conference on Machine Learning, pp. 445–453, Madison, Wis, USA, 1998.
- G. M. Weiss and F. Provost, “The effect of class distribution on classifier learning: an empirical study,” Tech. Rep. ML-TR-44, Dept. of Computer Science, Rutgers University, 2001.
- T. Fawcett, ROC Graphs: Notes and Practical Considerations for Data Mining Researchers, Intelligent Enterprise Technologies Lab, Hewlett-Packard, Palo Alto, Calif, USA, 2003.
- R. Ambrosino and B. G. Buchanan, “The use of physician domain knowledge to improve the learning of rule-based models for decision-support,” in Proceedings of Annual Fall Symposium American Medical Informatics Association, pp. 192–196, Washington, DC, USA, 1999.
- H. Daniels, A. Feelders, and M. Velikova, “Integrating economic knowledge in data mining algorithms,” in Proceedings of the 8th International Conference of the Society for Computational Economics: Computing in Economics and Finance, Aix-en-Provence, France, 2002.
- H. Langseth and T. D. Nielsen, “Fusion of domain knowledge with data for structural learning in object oriented domains,” Journal of Machine Learning Research, vol. 4, no. 3, pp. 339–368, 2004.
- S. M. Weiss, S. J. Buckley, S. Kapoor, and S. Damgaard, “Knowledge-based data mining,” in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '03), pp. 456–461, Washington, DC, USA, August 2003.
- Credit Union Journal, “Loan losses squeezing CUs,” American Banker, vol. 173, 227, p. 5, 2008.
- J. Bjorhus, “Bad loans sap banks’ profits,” Star Tribune, 2008.
- B. G. Buchanan and E. H. Shortliffe, Rule-Based Expert Systems: The MYCIN experiments of the Stanford Heuristic Programming Project, Addison-Wesley, Reading, Mass, USA, 1984.
- M. Stefik, Introduction to Knowledge Systems, Morgan Kaufmann, San Francisco, Calif, USA, 1995.
- D. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, Cambridge, Mass, USA, 2001.
- R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence, C. S. Mellish, Ed., pp. 1137–1143, Morgan Kaufmann, San Mateo, Calif, USA, 1995.