Abstract
It is well-known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. In many applications, however, a ranking based on class probabilities is desired. For example, a ranking of customers in terms of the likelihood that they buy one’s products is useful in direct marketing. What is the general performance of naive Bayes in ranking? In this paper, we study it by both empirical experiments and theoretical analysis. Our experiments show that naive Bayes outperforms C4.4, the most state-of-the-art decision-tree algorithm for ranking. We study two example problems that have been used in analyzing the performance of naive Bayes in classification [3]. Surprisingly, naive Bayes performs perfectly on them in ranking, even though it does not in classification. Finally, we present and prove a sufficient condition for the optimality of naive Bayes in ranking.
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Keywords
- Decision Tree
- Probability Estimate
- Decision Tree Algorithm
- Conditional Independence Assumption
- Decision Tree Learning
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zhang, H., Su, J. (2004). Naive Bayesian Classifiers for Ranking. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Machine Learning: ECML 2004. ECML 2004. Lecture Notes in Computer Science(), vol 3201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30115-8_46
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DOI: https://doi.org/10.1007/978-3-540-30115-8_46
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