Efficient Specific-to-General Rule Induction

Proceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

Volumn , Issue , 1996, Pages 319-322

  Domingos, Pedro ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

APPLICATIONS DOMAINS; CLASSIFICATION TIME; COMMON STRATEGY; ONE-RULE; RULE INDUCTION; RULE INDUCTION ALGORITHMS; RUNNING TIME; TIME BASED; TRAINING SETS; VERY LARGE DATABASE;

CLASSIFICATION (OF INFORMATION);

EID: 85173811900     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Breiman, L. Bagging predictors. Machine Learning. In press.
2. Catlett, J. 1991. Megainduction: Machine Learning on Very Large Databases. Ph.D. Dissertation, Basser Department of Computer Science, University of Sydney, Sydney, Australia.
3. Chan, P. K., and Stolfo, S. J. 1995. Learning arbiter and combiner trees from partitioned data for scaling machine learning. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, 39-44. Montreal, Canada: AAAI Press.
4. Clark, P., and Niblett, T. 1989. The CN2 induction algorithm. Machine Learning 3:261-283.
5. Cohen, W. W. 1995. Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning, 115-123. Tahoe City, CA: Morgan Kaufmann.
6. DeGroot, M. H. 1986. Probability and Statistics. Reading, MA: Addison-Wesley, 2nd edition.
7. Domingos, P. 1995. Rule induction and instance-based learning: A unified approach. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1226-1232. Montreal, Canada: Morgan Kaufmann.
8. Domingos, P. Unifying instance-based and rule-based induction. Machine Learning. In press.
9. Furnkranz, J., and Widmer, G. 1994. Incremental reduced error pruning. In Proceedings of the Eleventh International Conference on Machine Learning, 70-77. New Brunswick, NJ: Morgan Kaufmann.
10. Holte, R. C; Acker, L. E.; and Porter, B. W. 1989. Concept learning and the problem of small disjuncts. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 813-818. Detroit, MI: Morgan Kaufmann.
11. Murphy, P. M., and Aha, D. W. 1995. UCI repository of machine learning databases. Machine-readable data repository, Department of Information and Computer Science, University of California at Irvine, Irvine, CA.
12. Niblett, T. 1987. Constructing decision trees in noisy domains. In Proceedings of the Second European Working Session on Learning, 67-78. Bled, Yu goslavia: Sigma.
13. Pagallo, G., and Haussler, D. 1990. Boolean feature discovery in empirical learning. Machine Learning 3:71-99.
14. Quinlan, J. R. 1993. C4-5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
15. Stanfill, C., and Waltz, D. 1986. Toward memory-based reasoning. Communications of the ACM 29:1213-1228.
16. Wolpert, D. 1992. Stacked generalization. Neural Networks 5:241-259.