Learning with Rare Cases and Small Disjuncts

Proceedings of the 12th International Conference on Machine Learning, ICML 1995

Volumn , Issue , 1995, Pages 558-565

  Weiss, Gary M ^a  

^a LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATTRIBUTE CLASS; ATTRIBUTE NOISE; CLASS NOISE; CONCEPT DEFINITIONS; ERROR PRONES; INDUCTIVE LEARNING; LEARN+; SMALL DISJUNCT; TRAINING EXAMPLE; TRAINING SETS;

EID: 0002128687     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Ali, K. M. & Pazzani, M. J. (1992). Reducing the small disjuncts problem by learning probabilistic concept descriptions. Technical Report 92-111, Irvine, CA: University of California at Irvine, Department of Information and Computer Sciences. To appear in T. Petsche (ed.), Computational Learning Theory and Natural Learning Systems, Volume 5, Cambridge, Massachusetts. MIT Press.
2. Danyluk, A. P. & Provost, F. J. (1993). Small disjuncts in action: learning to diagnose errors in the local loop of the telephone network. In Machine Learning: Proceedings of the Tenth International Conference, 81-88, San Francisco, CA: Morgan Kaufmann.
3. Holte, R. C, Acker, L. E. & 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. San Mateo, CA: Morgan Kaufmann.
4. Quinlan, J. R. (1986). The effect of noise on concept learning. In R. S. Michalski, J. G. Carbonell & T. M. Mitchell (eds.), Machine Learning, an Artificial Intelligence Approach, Volume II, 149-166, Morgan Kaufmann.
5. Quinlan, J. R. (1991). Technical note: improved estimates for the accuracy of small disjuncts. Machine Learning, 6(1).
6. Quinlan, J. R. (1993). C45: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
7. Weiss, G. M. (1994). The problem with noise and small disjuncts. Technical Report ML-TR-38, New Brunswick, NJ: Rutgers University, Department of Computer Science.