Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Tree Induction for Probability-Based Ranking
Machine Learning
Inference for the Generalization Error
Machine Learning
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A comparative analysis of methods for probability estimation tree
WSEAS Transactions on Computers
Hi-index | 0.00 |
Naïve Bayes Tree uses decision tree as the general structure and deploys naïve Bayesian classifiers at leaves. The intuition is that naïve Bayesian classifiers work better than decision trees when the sample data set is small. Therefore, after several attribute splits when constructing a decision tree, it is better to use naïve Bayesian classifiers at the leaves than to continue splitting the attributes. In this paper, we propose a learning algorithm to improve the conditional probability estimation in the diagram of Naïve Bayes Tree. The motivation for this work is that, for cost-sensitive learning where costs are associated with conditional probabilities, the score function is optimized when the estimates of conditional probabilities are accurate. The additional benefit is that both the classification accuracy and Area Under the Curve (AUC) could be improved. On a large suite of benchmark sample sets, our experiments show that the CLL tree outperforms the state-of-art learning algorithms, such as Naïve Bayes Tree and naïve Bayes significantly in yielding accurate conditional probability estimation and improving classification accuracy and AUC.