Multistrategy approach to classification learning in databases

Data and Knowledge Engineering

Volumn 31, Issue 1, 1999, Pages 67-93

  Lee, Chang Hwan ^a   Shin, Dong Guk ^b  

^a Dongguk University   (South Korea)

^b UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DATABASE SYSTEMS; LEARNING SYSTEMS;

HELLINGER DIVERGENCE; RULE INDUCTION;

KNOWLEDGE ENGINEERING;

EID: 0032683212     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-023X(99)00018-X     Document Type: Article

References (37)

1. Aha D., Kibler D., Albert M. Instance-based learning algorithms. Machine Learning. 6(1):1991;37-66.
2. Beran R.J. Minimum Hellinger distances for parametric models. Ann. Statist. 5:1977;445-463.
3. L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and Regression Trees, Wadsworth, Belmont, 1984.
4. Cendrowska J. PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies. 27:1987;349-370.
5. K.C.C. Chan, A.K.C. Wong, A statistical technique for extracting classificatory knowledge from databases, in: G. Piatetsky-Shapiro, W.J. Frawley (Eds.), Knowledge Discovery in Databases, AAAI Press, New York, 1991, pp. 107-124.
6. Clark P., Niblett T. The CN2 induction algorithm. Machine Learning. 3:1989;261-283.
7. Cost S., Salzberg S. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning. 10:1993;57-78.
8. T.M. Cover, P.E. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory 13 (1967) 21-27.
9. DeJong G., Mooney R. Explanation-based learning: an alternative view. Machine Learning. 1:1986;145-176.
10. V. Dhar, A. Tuzhilin, Abstract-driven pattern discovery in databases, IEEE Transactions on Knowledge and Data Engineering 5 (6) (1993) 926-938.
11. J. Han, Y. Cai, N. Cercone, Data-driven discovery of quantitative rules in relational databases, IEEE Transactions on Knowledge and Data Engineering 5 (1) (1993) 29-40.
12. J.H. Holland, K.J. Holyoak, R.E. Nisbett, P.R. Thagard, Induction: Processes of Inference, Learning, and Discovery, MIT Press, Cambridge, MA, 1986.
13. Holte R. Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:1993;63-91.
14. Kan G., Visser C., Kooler J., Dunning A. Short and long term predictive value of wall motion score in acute myocardial infarction. British Heart Journal. 56:1986;422-427.
15. J.D. Kelly, L. Davis, A hybrid genetic algorithm for classification, in: Proceedings of The 12th International Conference on Artificial Intelligence, Morgan Kaufmann, Sydney, 1991, pp. 645-650.
16. C.H. Lee, D.G. Shin, A context-sensitive discretization of numeric attributes for classification learning, in: Proceedings of The 11th European Conference on Artificial Intelligence, Amsterdam, The Netherlands, 1994.
17. C.H. Lee, A hybrid approach for classification learning from databases, Ph.D. Thesis, Computer Science Department, University of Connecticut, 1994.
18. R.S. Michalski, I. Mozetic, J. Hong, N. Lavrac, The multi-purpose incremental learning system AQ15 and its testing application to three medical domains, in: Proceedings of The Fifth National Conference on Artificial Intelligence, Morgan Kaufmann, Philadelphia, PA, 1986, pp. 1041-1045.
19. S. Minton, J.G. Carbonell, O. Etzioni, C.A. Knoblock, D.R. Kuokka, Acquiring effective search control rules: explanation-based learning in the PRODIGY system, in: Proceedings of The fourth International Machine Learning Workshop, 1987.
20. R.M. Nosofsky, Attention, similarity, and the identification-categorization relationship, Journal of Experimental Psychology: General 115 (1986) 39-57.
21. Ourston D., Mooney R.J. Theory refinement combining analytical and empirical methods. Artificial Intelligence. 66:1994;273-309.
22. M.J. Pazzani, Integrating explanation-based and empirical learning methods in OCCAM, in: Proceedings of EWSL, 1988.
23. Pazzani M.J., Kibler D. The utility of knowledge in inductive learning. Machine Learning. 9:1992;57-94.
24. G. Piatetsky-Shapiro, Discovery, Analysis, and Presentation of Strong Rules, in: Piatetsky-Shapiro, W.J. Frawley (Eds.), Knowledge Discovery in Databases, AAAI Press, New York, 1991, pp. 229-248.
25. J.R. Quinlan, Generating production rules from decision trees, in: Proceedings of The International Joint Conference Artificial Intelligence, 1987, pp. 304-307.
26. Quinlan J.R. Induction of decision trees. Machine Learning. 1:1986;81-106.
27. D.E. Rumelhart, J.L. McClelland, The PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, MIT Press, Cambridge, MA, 1987.
28. S. Salzberg, Exemplar-based learning: theory and implementation, Technical Report TR-10-88, Center for Research in Computing Technology, Aiken Computation Laboratory, Harvard University, 1988.
29. E.E. Smith, D.L. Medin, Categories and Concepts, Harvard Univ. Press, Cambridge, MA, 1981.
30. P. Smyth, R.M. Goodman, An information theoretic approach to rule induction from databases, IEEE Transactions on Knowledge and Data Engineering 4 (4) (1992) 301-316.
31. Stanfill C., Waltz D. Toward memory-based reasoning. Communications of the ACM, 29(12):1986;1213-1228.
32. M. Tan, L. Eshelman, Using weighted networks to represent classification knowledge in noisy domains, in: Proceedings of The fifth International Conference on Machine Learning, 1988, pp. 121-134.
33. S.M. Weiss, N. Indurkhya, Optimized rule induction, IEEE Expert, December, 1993, pp. 61-69.
34. D. Wettschereck, D. Aha, Weighting features, in: Proceedings of The First International Conference on Case-Based Reasoning, 1995.
35. B.L. Whitehall, Knowledge-based Learning: integration and deductive and inductive learning for knowledge base completion, Ph.D. Thesis, Computer Science Department, University of Illinois, 1990.
36. W.H. Wolberg, O.L. Mangasarian, Multisurface method of pattern separation for medical diagnosis applied to breast cytology, in: Proceedings of The National Academy of Sciences, vol. 87, 1990, pp. 9193-9196.
37. J. Zhang, Selecting typical instances in instance-based learning, in: Proceedings of The Ninth International Machine Learning Conference, Morgan Kaufmann, Aberdeen, Scotland, 1992, pp. 470-479.