Improving the reliability of decision tree and naive bayes learners

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 459-462

  Lindsay, David ^a   Cox, Siân ^a  

^a UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; EMPIRICAL RELIABILITY CURVES (ERC); NAVIE BAYES LEARNERS; RECEIVER OPERATOR CHARACTERISTICS (ROC) CURVES; VENN PROBABILITY MACHINES (VPM) META-LEARNERS;

CALIBRATION; COMPUTATIONAL COMPLEXITY; DATA MINING; DATABASE SYSTEMS; DECISION THEORY; FORECASTING; LAPLACE TRANSFORMS; LEARNING SYSTEMS; PROBABILITY; RELIABILITY THEORY; SET THEORY; TREES (MATHEMATICS);

PATTERN RECOGNITION;

EID: 19544394588     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2004.10037     Document Type: Conference Paper

References (11)

1. C. G. Atkeson, A. W. Moore, and S. Schaal. Locally weighted learning. Artificial Intelligence Review, 11:11-73, 1997.
2. B. Cestnik. Estimating probabilities: A crucial task in machine learning. In Proc. of the 9th Euro. Conf. on Artificial Intelligence, 1990.
3. A. P. Dawid. Calibration-based empirical probability (with discussion). Annals of Statistics, 13:1251-1285, 1985.
4. M. H. DeGroot and S. E. Feinberg. The comparison and evaluation of forecasters. The Statistician, 32:12-22, 1982.
5. U. Fayyad and K. Irani. The attribute selection problem in decision tree generation. In Proc. of 10th Nat. Conf. on Artificial Intelligence, 1992.
6. D. Lindsay. Visualising and improving reliability - a machine learning perspective. CLRC-TR-04-01, Royal Holloway University of London, England, 2004.
7. A. H. Murphy. A new vector partition of the probability score. Journal of Applied Meteorology, 12:595-600, 1973.
8. F. Provost and T. Fawcett. Analysis and visualisation of classifier performance: Comparision under imprecise class and cost distributions. In Proc. of the 3rd Int. Conf. on Knowledge Discovery and Data Mining, 1997.
9. V. Vovk, G. Shafer, and I. Nouretdinov. Self-calibrating probability forecasting. In Advances in Neural Information Processing Systems 16, 2003.
10. I. Witten and E. Frank. Data Mining - Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, 2000.
11. B. Zadrozny and C. Elkan. Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In Proc. of the 18th Int. Conf. on Machine Learning, 2001.