NDLI: Augmenting naive Bayes for ranking

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Augmenting naive Bayes for ranking

Content Provider	ACM Digital Library
Author	Su, Jiang Zhang, Harry Jiang, Liangxiao
Abstract	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.
Starting Page	1020
Ending Page	1027
Page Count	8
File Format	PDF
ISBN	1595931805
DOI	10.1145/1102351.1102480
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-07
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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