C4.5: programs for machine learning
C4.5: programs for machine learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Conditional Independence Tree for Ranking
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Learning probabilistic decision trees for AUC
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Decision Trees for Probability Estimation: An Empirical Study
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Representing conditional independence using decision trees
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning naïve bayes tree for conditional probability estimation
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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In this paper, we address the problem of probability estimation of decision trees. This problem has received considerable attention in the areas of machine learning and data mining, and techniques to use tree models as probability estimators have been suggested. We make a comparative study of six well-known class probability estimation methods, measured by classification accuracy, AUC and Conditional Log Likelihood (CLL). Comments on the properties of each method are empirically supported. Our experiments on UCI data sets and our liver disease data sets show that the PETs algorithms outperform traditional decision trees and naïve Bayes significantly in classification accuracy, AUC and CLL respectively. Finally, a unifying pseudocode of algorithm is summarized in this paper.