Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Survey of Improving Naive Bayes for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Improve the Accuracy of One Dependence Augmented Naive Bayes by Weighted Attribute
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Random one-dependence estimators
Pattern Recognition Letters
One Dependence Value Difference Metric
Knowledge-Based Systems
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In real-world data mining applications, an accurate ranking is as important as an accurate classification. Naive Bayes has been widely used in data mining as a simple and effective classification and ranking algorithm. Since its conditional independence assumption is rarely true, numerous algorithms have been proposed to improve naive Bayes, for example, SBC[1] and TAN[2]. Indeed, the experimental results show that SBC and TAN achieve a significant improvement in term of classification accuracy. However, unfortunately, our experiments also show that SBC and TAN perform even worse than naive Bayes in ranking measured by AUC[3,4](the area under the Receiver Operating Characteristics curve). This fact raises the question of whether we can improve Naive Bayes with both accurate classification and ranking? In this paper, responding to this question, we present a new learning algorithm called One Dependence Augmented Naive Bayes(ODANB). Our motivation is to develop a new algorithm to improve Naive Bayes' performance not only on classification measured by accuracy but also on ranking measured by AUC. We experimentally tested our algorithm, using the whole 36 UCI datasets recommended by Weka[5], and compared it to Naive Bayes, SBC and TAN. The experimental results show that our algorithm outperforms all the other algorithms significantly in yielding accurate ranking, yet at the same time outperforms all the other algorithms slightly in terms of classification accuracy.