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Abstract: . The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier's probability estimates are only optimal under... (Update)
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BibTeX entry: (Update)
Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103--130. http://citeseer.ist.psu.edu/domingos97optimality.html More
@article{ domingos97optimality,
author = "Pedro Domingos and Michael J. Pazzani",
title = "On the Optimality of the Simple Bayesian Classifier under Zero-One Loss",
journal = "Machine Learning",
volume = "29",
number = "2-3",
pages = "103-130",
year = "1997",
url = "citeseer.ist.psu.edu/domingos97optimality.html" }
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