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
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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
Eighteenth national conference on Artificial intelligence
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
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Survey of Improving Naive Bayes for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Ordinal extreme learning machine
Neurocomputing
A Chinese web page automatic classification system
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Bayesian classifiers for positive unlabeled learning
WAIM'11 Proceedings of the 12th international conference on Web-age information management
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Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is more desirable. Unfortunately, naive Bayes has been found to produce poor probability estimates. Numerous techniques have been proposed to extend naive Bayes for better classification accuracy, of which selective Bayesian classifiers (SBC) (Langley & Sage, 1994), tree-augmented naive Bayes (TAN) (Friedman et al., 1997), NBTree (Kohavi, 1996), boosted naive Bayes (Elkan, 1997), and AODE (Webb et al., 2005) achieve remarkable improvement over naive Bayes in terms of classification accuracy. An interesting question is: Do these techniques also produce accurate ranking? In this paper, we first conduct a systematic experimental study on their efficacy for ranking. Then, we propose a new approach to augmenting naive Bayes for generating accurate ranking, called hidden naive Bayes (HNB). In an HNB, a hidden parent is created for each attribute to represent the influences from all other attributes, and thus a more accurate ranking is expected. HNB inherits the structural simplicity of naive Bayes and can be easily learned without structure learning. Our experiments show that HNB outperforms naive Bayes, SBC, boosted naive Bayes, NBTree, and TAN significantly, and performs slightly better than AODE in ranking.