Lookahead-based algorithms for anytime induction of decision trees

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 257-264

  Esmeir, Saher ^a   Markovitch, Shaul ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; CONSTRAINT THEORY; DECISION MAKING; HEURISTIC METHODS; PROBLEM SOLVING; TREES (MATHEMATICS);

CONSTRAINED ALGORITHMS; DECISION TREES; EXPONENTIAL COMPLEXITY; TREE QUALITY;

LEARNING SYSTEMS;

EID: 14344253489     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Baram, Y., El-Yaniv, R., & Luz, K. (2003). Online choice of active learning algorithms. ICML'03 (pp. 19-26).
2. Blake, C. L., & Merz, C. J. (1998). UCI repository of machine learning databases.
3. Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987). Occam's Razor. Information Processing Letters, 24, 377-380.
4. Boddy, M., & Dean, T. L. (1994). Deliberation scheduling for problem solving in time constrained environments. Artificial Intelligence, 67, 245-285.
5. Bouckaert, R. R. (2003). Choosing between two learning algorithms based on calibrated tests. ICML'03 (pp. 51-58).
6. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Monterey, CA: Wadsworth and Brooks.
7. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
8. Dong, M., & Kothari, R. (2001). Look-ahead based fuzzy decision tree induction. IEEE-FS, 9, 461-468.
9. Hyafil, L., & Rivest, R. L. (1976). Constructing optimal binary decision trees is NP-complete. Information Processing Letters, 5, 15-17.
10. Kononenko, I., & Kovacic, M. (1992). Learning as optimization: Stochastic generation of multiple knowledge. ICML'92 (pp. 257-262).
11. Minsky, M., & Papert, S. (1969). Perceptrons: Introduction to computational geometry. MIT Press, Cambridge, MA.
12. Murphy, O. J., & McCraw, R. L. (1991). Designing storage efficient decision trees. IEEE Transactions on Computers, 40, 315-320.
13. Murphy, P. M., & Pazzani, M. J. (1994). Exploring the decision forest: an empirical investigation of Occam's Razor in decision tree induction. Journal of Artificial Intelligence Research, 1, 257-275.
14. Murthy, S. K., & Salzberg, S. (1995). Lookahead and pathology in decision tree induction. IJCAI'95 (pp. 1025-1033).
15. Norton, S. W. (1989). Generating better decision trees. IJCAI'89 (pp. 800-805).
16. Page, D., & Ray, S. (2003). Skewing: An efficient alternative to lookahead for decision tree induction. IJCAI'03.
17. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
18. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
19. Ragavan, H., & Rendell, L. (1993). Lookahead feature construction for learning hard concepts. ICML'93 (pp. 252-259).
20. Russell, S. J., & Zilberstein, S. (1991). Composing real-time systems. IJCAI'91 (pp. 212-217).
21. Sarkar, U. K., Chakrabarti, P., Ghose, S., & DeSarkar, S. C. (1994). Improving greedy algorithms by look-ahead search. Journal of Algorithms, 16, 1-23.
22. Schapire, R. (1999). A brief introduction to boosting. IJCAI'99 (pp. 1401-1406).
23. Webb, G. I. (1996). Further experimental evidence against the utility of Occam's Razor. Journal of Artificial Intelligence Research, 4, 397-417.