Optimized Rule Induction

IEEE Expert-Intelligent Systems and their Applications

Volumn 8, Issue 6, 1993, Pages 61-69

  Weiss, Sholom M ^a   Indurkhya, Nitin ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0040452732     PISSN: 08859000     EISSN: None     Source Type: Journal    
DOI: 10.1109/64.248354     Document Type: Article

References (33)

1. C. Wallace and P. Freeman, “Estimation and Inference by Compact Encoding,” J. Royal Statistical Soc. Series B., Vol. 49B, No. 3, 1987, pp. 240–265.
2. J. Rissanen, “Modeling by Shortest Data Description,” Automatica, Vol. 14, 1978, pp. 465–471.
3. J. Rissanen, “Stochastic Complexity.” J. Royal Statistical Soc. Series B., Vol. 49, No. 3, 1987, pp. 223–239.
4. R. Holte, “Very Simple Classification Rules Perform Well on Most Data Sets,” Tech. Report TR-91-16, Computer Science Dept., U. of Ottawa, Canada, 1991. To be published in Machine Learning.
5. L. Breiman et al., Classification and Regression Trees, Wadsworth, Monterrey, Calif., 1984.
6. J. Quinlan, “Simplifying Decision Trees,” Int’l J. Man-Machine Studies, Vol.27,1987, pp. 221–234.
7. M. James, Classification Algorithms, John Wiley & Sons, New York, 1985.
8. S. Weiss and I. Kapouleas, “An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods,” Int’l Joint Conf. Artificial Intelligence (IJCAI-89), Morgan Kaufmann, San Mateo, Calif., 1989, 781–787.
9. S. Weiss, R. Galen, and P. Tadepalli, “Maximizing the Predictive Value of Production Rules,” Artificial Intelligence, Vol. 45,1990, pp. 47–71.
10. J. Quinlan, “Generating Production Rules from Decision Trees,” Proc. Int’l Joint Conf. Artificial Intelligence (IJCAI-87), Morgan Kaufmann, San Mateo, Calif., 1987, pp. 304–307.
11. P. Clark and T. Niblett, “The CN2 Induction Algorithm,” Machine Learning, Vol. 3, 1989, pp. 261–283.
12. G. Pagallo and D. Haussler, “Boolean Feature Discovery in Empirical Learning,” Machine Learning, Vol. 5, No. 1, 1990, pp. 71–99.
13. R. Michalski et al., “The Multipurpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains,” Nat’l Conf. Artificial Intelligence (AAAI-86), MIT Press, Cambridge, Mass., 1986, pp. 1041-1045.
14. N. Indurkhya and S. Weiss, “Iterative Rule Induction Methods,” Applied Intelligence, Vol. 1, 1991, pp. 43–54.
15. G. Pagallo, “Learning DNF by Decision Trees,” Proc. Int’l Joint Conf. Artificial Intelligence '89, Morgan Kaufmann, San Mateo, Calif., 1989, 639–644.
16. S. Lin and B. Kernighan, “An Efficient Heuristic for the Traveling Salesman Problem,” Operations Research, Vol. 21, No. 2, 1973, pp. 498–516.
17. T. Hastie and R. Tibshirani, Generalized Additive Models, Chapman and Hall, London, 1990.
18. W. Highleyman, “The Design and Analysis of Pattern Recognition Experiments,” Bell System Tech. J., Vol. 41, 1962, pp. 723–744.
19. M. Stone, “Cross-Validatory Choice and Assessment of Statistical Predictions,” J. the Royal Statistical Society, Vol. 36,1974, pp. 111–147.
20. P. Utgoff, “Perceptron Trees: A Case Study in Hybrid Concept Representation,” Proc. Nat’l Conf. Artificial Intelligence (AAAI-88), Morgan Kaufmann, San Mateo, Calif., 1988, pp. 601–606.
21. R. Fisher, “The Use of Multiple Measurements in Taxonomic Problems,” Annals of Eugenics, Vol. 7, 1936, pp. 179–188.
22. C. Apte, S. Weiss, and G. Grout, “Predicting Defects in Disk Drive Manufacturing: A Case Study in High-Dimensional Classification,” Proc. IEEE Computer Soc. Conf. on Artificial Intelligence for Applications (CAIA-93), IEEE Computer Soc. Press, Los Alamitos, Calif., 1993, pp. 212–218.
23. C. Apte, F. Damerau, and S. Weiss, “Auto-mated Learning of Decision Rules for Text Categorization,” to be published in ACM Trans, on Office Information Systems, 1994.
24. R. Sasisekharan, V. Seshadri, and S.M. Weiss, “Proactive Network Maintenance Using Machine Learning,” to appear in Proc. IEEE Globecom ’93, IEEE Service Center, Piscataway, N.J., 1993.
25. T. Sejnowski and R. Rosenberg, “Parallel Networks that Learn to Pronounce English Text,” Complex Systems, Vol. 1, 1987, pp. 145–168.
26. T. Dietterich, H. Hild, and G. Bakin, “A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping,” Proc. Seventh Int’l Conf. Machine Learning, Morgan Kaufmann, San Mateo, Calif., 1990, pp. 24–31.
27. T. Dietterich and G. Bakiri, “Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs,” Proc. Nat’l Conf. Artificial Intelligence '91, Morgan Kaufmann, San Mateo, Calif., 1991, pp. 572–577.
28. R. Detrano et al., “Int’l Application of a New Probability Algorithm for the Diagnosis of Coronary Artery Disease,” Amer. J. Cardiology, Vol. 64, 1989, pp. 304–310.
29. J. Shavlik, R. Mooney, and G. Towell, “Symbolic and Neural Learning Algorithms: An Experimental Comparison,” Machine Learning, Vol. 6, 1991.
30. G. Towell, J. Shavlik, and M. Noordeweir, “Refinement of Approximate Domain Theories by Knowledge-Based Neural Networks,” Proc. Nat’l Conf Artificial Intelligence '90, Morgan Kaufmann, San Mateo, Calif., 1990, pp. 861–866.
31. D. Lindberg et al., “Computer-BasedRheu-matology Consultant,” Medinfo-80: Proc. Third World Conf. Medical Informatics 1980, North Holland, Amsterdam, 1980, pp. 1311-1315.
32. A. Ginsberg, S. Weiss, and P. Politakis, “Automatic Knowledge Base Refinement for Classification Systems,” Artificial Intelligence, Vol. 35, 1988, pp. 197–226.
33. A. Ginsberg, “Theory Reduction, Theory Revision, and Retranslation,” Proc. Nat’l Conf. Artificial Intelligence '90, Morgan Kaufmann, San Mateo, Calif., 1990, pp. 777–782.