Uncertainty in Artificial Intelligence
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Locally Weighted Naive Bayes
Eibe Frank, Mark Hall, Bernhard Pfahringer
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness --- attribute independence --- and improve the performance of the algorithm. This paper presents a locally weighted version of naive Bayes that relaxes the independence assumption by learning local models at prediction time. Experimental results show that locally weighted naive Bayes rarely degrades accuracy compared to standard naive Bayes and, in many cases, improves accuracy dramatically. The main advantage of this method compared to other techniques for enhancing naive Bayes is its conceptual and computational simplicity
Keywords: Naive Bayes, Locally Weighted Learning
Pages: 249-256
PS Link: http://www.cs.waikato.ac.nz/~eibe/pubs/UAI_200.ps.gz
PDF Link: /papers/03/p249-frank.pdf
AUTHOR = "Eibe Frank and Mark Hall and Bernhard Pfahringer",
TITLE = "Locally Weighted Naive Bayes",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "249--256"

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