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| O. Gur-Ali, W.A. Wallace, "Induction of Rules Subject to a Quality Constraint: Probabilistic Inductive Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 6, pp. 979-984, December, 1993. | |||
| BibTex | x | ||
| @article{ 10.1109/69.250081, author = {O. Gur-Ali and W.A. Wallace}, title = {Induction of Rules Subject to a Quality Constraint: Probabilistic Inductive Learning}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {5}, number = {6}, issn = {1041-4347}, year = {1993}, pages = {979-984}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.250081}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Knowledge and Data Engineering TI - Induction of Rules Subject to a Quality Constraint: Probabilistic Inductive Learning IS - 6 SN - 1041-4347 SP979 EP984 EPD - 979-984 A1 - O. Gur-Ali, A1 - W.A. Wallace, PY - 1993 KW - quality constraint; probabilistic inductive learning; organizational databases; rule induction; decision processes; tree induction algorithms; total branching; subset elimination; rule generation; maximum misclassification levels; minimum reliability levels; automated decision processes; claims process; compensation board; decision support system; knowledge acquisition; statistical quality control; decision support systems; deductive databases; learning (artificial intelligence); tree data structures; uncertainty handling VL - 5 JA - IEEE Transactions on Knowledge and Data Engineering ER - | |||
Organizational databases are being used to develop rules or guidelines for action that are incorporated into decision processes. Tree induction algorithms of two types, total branching and subset elimination, used in the generation of rules, are reviewed with respect to their treatment of the issue of quality. Based on this assessment, a hybrid approach, probabilistic inductive learning (PrIL), is presented. It provides a probabilistic measure of goodness for an individual rule, enabling the user to set maximum misclassification levels, or minimum reliability levels, with predetermined confidence that each and every rule will satisfy this criterion. The user is able to quantify the reliability of the decision process, i.e., the invoking of the rules, which is of crucial importance in automated decision processes. PrIL and its associated algorithm are described. An illustrative example based on the claims process at a workers' compensation board is presented.
[1] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone,Classification and Regression Trees. Belmont, CA: Wadsworth Int., 1983.
[2] C. Carter and J. Carlett, "Assessing credit card applications using machine learning,"IEEE Expert, vol. 2, no. 3, pp. 71-79, 1987.
[3] K. C. C. Chan and A. K. C. Wong, "A statistical technique for extracting classificatory knowledge from databases," inKnowledge Discovery in Databases, G. Pietsky-Shapiro and W. J. Frowley, Eds. Cambridge, MA: AAAI/MIT Press, 1991.
[4] R. Christensen,Log-Linear Models. New York: Springer-Verlag, 1990.
[5] M. H. DeGroot,Probability and Statistics. Reading, MA: Addison-Wesley, 1989.
[6] S. B. Gelfand, C. S. Ravishankar, and E. J. Delp, "An iterative growing and pruning algorithm for classification design,"IEEE Trans. Pattern Anal. Machine Intell., vol. 13, no. 2, pp. 163-174, 1991.
[7] F.Ö. Gür, "The use of exploratory data analysis to develop decision rules for claims classification at a Workers' Compensation Board," unpublished Master's thesis, Decision Sciences and Eng. Syst., Rensselaer Polytechnic Inst., 1990.
[8] A. Hart, "Experience in the use of an inductive system in knowledge engineering," inResearch and Developments in Expert Systems, M. Bramer., Ed. Cambridge, U.K.: Cambridge Univ. Press, 1984.
[9] I. Konokenko, I. Bratko, and E. Roskar, "Experiments in automatic learning of medical diagnostic rules," Jozef Stefan Inst., Yugoslavia, Tech. Rep., 1984.
[10] T. Liang, "A composite approach to inducing knowledge for expert systems design,"Management Sci., vol. 38, no. 1, pp. 1-17, 1992.
[11] J. Mingers, "Expert systems-Rule induction with statistical data,"J. Oper. Res., vol. 38, no. 1, pp. 39-47, 1987.
[12] J. Mingers, "Empirical comparison of selection measures for decision tree induction,"Machine Learning, vol. 3, pp. 319-342, 1989.
[13] A. Patterson and T. Niblett,ACLS User Manual. Glasgow, Scotland: Intelligent Terminal Ltd., 1982.
[14] J. R. Quinlan, "Induction of decision trees,"Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
[15] J. Quinlan, "Simplifying decision trees,"Int. J. Man-Machine Studies, vol. 27, pp. 221-234, 1987.
[16] J. R. Quinlan, "Decision trees as probabilistic classifiers," inProc. 4th Workshop Machine Learning, P. Langley, Ed. Los Altos, CA: Morgan Kaufmann, 1987.
[17] J. R. Quinlan, "Decision trees and decision making,"IEEE Trans. Syst., Man, Cybern., vol. 20, no. 2, pp. 339-346, 1990.
[18] S. R. Safavian and D. A. Landgrebe, "A survey of decision tree classifier methodology,"IEEE Trans. Syst., Man, Cybern., vol. 21, no. 3, pp. 660-674, 1991.
[19] I. K. Sethi and G. P. R. Sarvarayudu, "Hierarchical classifier design using mutual information, "IEEE Trans. Partern Anal. Machine Intell., vol. PAMI-4, pp. 441-445, 1982.
[20] R. Uthurusamy, U. M. Fayyad, and S. Spangler, "Learning useful rules from inconclusive data," inKnowledge Discovery in Databases, G. Pietsky-Shapiro and W. J. Frowley, Eds. Cambridge, MA: AAAI/MIT Press, 1991.