Pre-pruning classification trees to reduce overfitting in noisy domains

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2412, Issue , 2002, Pages 7-12

  Bramer, Max ^a  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; INFORMATION THEORY;

AUTOMATIC INDUCTION; CLASSIFICATION RULES; CLASSIFICATION TREES; INFORMATION CONTENTS; PREDICTIVE ACCURACY; PREDICTIVE VALUES; SUBSTANTIAL REDUCTION; TRAINING DATA;

TREES (MATHEMATICS);

EID: 84866008319     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45675-9_2     Document Type: Conference Paper

References (7)

1. Hunt, E.B., Marin J. and Stone, P.J. (1966). Experiments in Induction. Academic Press
2. Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann
3. Bramer, M.A. (2002). An Information-Theoretic Approach to the Pre-pruning of Classification Rules. Proceedings of the IFIP World Computer Congress, Montreal 2002.
4. Blake, C.L. and Merz, C.J. (1998). UCI Repository of Machine Learning Databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science
5. Mingers, J. (1989). An Empirical Comparison of Pruning Methods for Decision Tree Induction. Machine Learning, 4, pp. 227-243
6. Bramer, M.A. (2002). Using J-Pruning to Reduce Overfitting in Classification Trees. In: Research and Development in Intelligent Systems XVIII. Springer-Verlag, pp. 25-38.
7. Smyth, P. and Goodman, R.M. (1991). Rule Induction Using Information Theory. In: Piatetsky-Shapiro, G. and Frawley, W.J. (eds.), Knowledge Discovery in Databases. AAAI Press, pp. 159-176