Adjusting for Multiple Comparisons in Decision Tree Pruning

Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Volumn , Issue , 1997, Pages 195-198

  Jensen, David ^a   Schmill, Matt ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

CANDIDATE MODELS; INDUCTION ALGORITHMS; MODELING ACCURACY; MULTIPLE COMPARISON; OVERFITTING; PRUNING TECHNIQUES; TREE PRUNING;

DECISION TREES;

EID: 0343109727     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

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3. Feelders, A., and Verkooijen, W. 1995. Which method learns the most from data? methodological issues in the analysis of comparative studies. In Preliminary Papers of the Fifth International Workshop on Artificial Intelligence and Statistics.
4. Gascuel, O., and Caraux, G. 1992. Statistical significance in inductive learning. In ECAI92: Proceedings of the 10th European Conference on Artificial Intelligence, 435-439.
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6. Kass, G. 1980. An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29:199-127.
7. Kerber, R. 1992. Chimerge: Discretization of numeric attributes. In Proceedings of the Tenth National Conference on Artificial Intelligence, 123-128.
8. Kotz, S., and Johnson, N., eds. 1982. Encyclopedia of Statistical Sciences. Wiley.
9. Oates, T., and Jensen, D. 1997. The effects of training set size on decision tree complexity. In To appear in the Proceedings of the Fourteenth International Conference on Machine Learning.
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11. Quinlan, J. 1986. Induction of decision trees. Machine Learning 1:81-106.
12. Quinlan, J. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
13. Utgoff, P. 1995. Decision tree induction based on efficient tree restructuring. Technical Report 95-18, Department of Computer Science, University of Massachusetts, Amherst.