C4.5: programs for machine learning
C4.5: programs for machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
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
Technical Note: Naive Bayes for Regression
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Tree Induction for Probability-Based Ranking
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Learning tree augmented naive bayes for ranking
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
A fast subspace text categorization method using parallel classifiers
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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It is well known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. AUC (the area under the receiver operating characteristics curve) is a measure different from classification accuracy and probability estimation, which is often used to measure the quality of rankings. Indeed, an accurate ranking of examples is often more desirable than a mere classification. What is the general performance of naive Bayes in yielding optimal ranking, measured by AUC? In this paper, we study it systematically by both empirical experiments and theoretical analysis. In our experiments, we compare naive Bayes with a state-of-the-art decision-tree learning algorithm C4.4 for ranking, and some popular extensions of naive Bayes which achieve a significant improvement over naive Bayes in classification, such as the selective Bayesian classifier (SBC) and tree-augmented naive Bayes (TAN). Our experimental results show that naive Bayes performs significantly better than C4.4 and comparably with TAN. This provides empirical evidence that naive Bayes performs well in ranking. Then we analyse theoretically the optimality of naive Bayes in ranking. We study two example problems: conjunctive concepts and m-of-n concepts, which have been used in analysing the performance of naive Bayes in classification. Surprisingly, naive Bayes performs optimally on them in ranking, even though it does not in classification. We present and prove a sufficient condition for the optimality of naive Bayes in ranking. From both empirical and theoretical studies, we believe that naive Bayes is a competitive model for ranking.