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Abstract: The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been proposed so far. In this paper we show that the SBC does not in fact assume attribute independence, and can be optimal even when this assumption is violated by a wide margin. The key to this finding lies in... (Update)
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BibTeX entry: (Update)
Domingos, P. & Pazzani, M. (1996), Beyond independence: conditions for the optimality of the simple Bayesian classifier, in L. Saitta, ed., `Machine Learning: Proceedings of the Thirteenth International Conference', Morgan Kaufmann, pp. 105--112. http://citeseer.ist.psu.edu/domingos96beyond.html More
@inproceedings{ domingos96beyond,
author = "Pedro Domingos and Michael J. Pazzani",
title = "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier",
booktitle = "International Conference on Machine Learning",
pages = "105-112",
year = "1996",
url = "citeseer.ist.psu.edu/domingos96beyond.html" }
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