NDLI: Learning lazy naive Bayesian classifiers for ranking

Content Provider	IEEE Xplore Digital Library
Author	Liangxiao Jiang Yuanyuan Guo
Copyright Year	2005
Description	Author affiliation: Fac. of Comput. Sci., China Univ. of Geosciences, Hubei (Liangxiao Jiang; Yuanyuan Guo)
Abstract	Naive Bayes (simply NB) has been well-known as an effective and efficient classification algorithm. However, it is based on the conditional independence assumption that it is often violated in applications. In addition, in many real-world data mining applications, however, an accurate ranking of instances is often required rather than an accurate classification. For example, a ranking of customers in terms of the likelihood that they buy one's products is useful in direct marketing. In this paper, we firstly investigate the ranking performance of some lazy learning algorithms for extending naive Bayes. The ranking performance is measured by Hand and Till (2001) and Bradley (1997). We observe that they can not significantly improve naive Bayes' ranking performance. Motivated by this fact and aiming at improving naive Bayes with accurate ranking, we present a new lazy learning algorithm, called lazy naive Bayes (simply LNB), to extend naive Bayes for ranking. We experimentally tested our algorithm, using the whole 36 UCI data sets (Blake and Merz, 2000) recommended by Weka, and compared it to NB and C4.4 (Provost and Domingos, 2003) measured by AUC. The experimental results show that our algorithm significantly outperforms both NB and C4.4
Starting Page	5
Ending Page	416
File Size	152909
Page Count	412
File Format	PDF
ISBN	0769524885
ISSN	10823409
DOI	10.1109/ICTAI.2005.80
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Niobium Data mining Error analysis Application software Testing Computer science Geology Classification algorithms Equations
Content Type	Text
Resource Type	Article

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