Inducing interpretable voting classifiers without trading accuracy for simplicity: Theoretical results, approximation algorithms, and experiments

Journal of Artificial Intelligence Research

Volumn 17, Issue , 2002, Pages 137-170

  Nock, Richard ^a  

^a UNIVERSITÉ DES ANTILLES ET DE LA GUYANE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DECISION MAKING; HEURISTIC METHODS; LEARNING SYSTEMS; SET THEORY; TREES (MATHEMATICS);

DECISION COMMITTEES; DECISION TREE; ENSEMBLE CLASSIFIERS; VOTING CLASSIFIERS;

ALGORITHMS;

EID: 3242735194     PISSN: 10769757     EISSN: None     Source Type: Journal    
DOI: 10.1613/jair.986     Document Type: Article

References (38)

1. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36, 105-139.
2. Blake, C. L., Keogh, E., & Merz, C. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html.
3. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140.
4. Breiman, L., Freidman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth.
5. th International Conference on Knowledge Discovery in Databases, pp. 27-36.
6. Buntine, W., & Niblett, T. (1992). A further comparison of splitting rules for Decision-Tree induction. Machine Learning, 8, 75-85.
7. th European Working Session in Learning, pp. 155-161.
8. th National Conference on Artificial Intelligence, pp. 335-342.
9. de Carvalho Gomes, F. A., & Gascuel, O. (1994). SDL, a stochastic algorithm for learning decision lists with limited complexity. Annals of Mathematics and Artificial Intelligence, 10, 281-302.
10. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40, 139-157.
11. th International Conference on Machine Learning, pp. 127-135.
12. th ACM Symposium on the Theory of Computing, pp. 314-318.
13. th International Conference on Machine Learning, pp. 152-160.
14. th International Conference on Machine Learning, pp. 124-133.
15. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics, 28, 337-374.
16. Garey, M., & Johnson, D. (1979). Computers and Intractability, a guide to the theory of NP-Completeness. Bell Telephone Laboratories.
17. Grötschel, M., Lovàsz, L., & Schrijver, A. (1981). The ellipsoid method and its consequences in combinatorial optimization. Combinatorica, 1, 169-197.
18. Holte, R. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63-91.
19. Hyafil, L., & Rivest, R. (1976). Constructing optimal decision trees is NP-complete. Information Processing Letters, 5, 15-17.
20. th International Conference on Machine Learning, pp. 121-129.
21. th International Conference on Machine Learning, pp. 269-277.
22. th ACM Symposium on the Theory of Computing, pp. 285-295.
23. th International Conference on Knowledge Discovery in Databases, pp. 249-253.
24. th International Conference on Computational Learning Theory, pp. 220-224.
25. th International Conference on Machine Learning, pp. 211-218.
26. Mitchell, T. (1997). Machine Learning. McGraw-Hill.
27. th International Conference on Machine Learning, pp. 413-420. Morgan Kaufmann.
28. th International Conference on Machine Learning, pp. 413-420. Morgan Kaufmann.
29. Opitz, D., & Maclin, R. (1999). Popular ensemble methods: a survey. Journal of Artificial Intelligence Research, 11, 169-198.
30. Queyranne, M. (1998). Minimizing symmetric submodular functions. Mathematical Programming, 82, 3-12.
31. Quinlan, J. R. (1994). C4.5: programs for machine learning. Morgan Kaufmann.
32. th National Conference on Artificial Intelligence, pp. 725-730.
33. th International Conference on Knowledge Discovery in Databases, pp. 101-104.
34. Rivest, R. (1987). Learning decision lists. Machine Learning, 2, 229-246.
35. Schapire, R. E., Freund, Y., Bartlett, P., & Lee, W. S. (1998). Boosting the Margin: a new explanation for the effectiveness of Voting methods. Annals of statistics, 26, 1651-1686.
36. th International Conference, on Computational Learning Theory, pp. 80-91.
37. Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27, 1134-1142.
38. th International Joint Conference on Artificial Intelligence, pp. 560-566.