Noisy-or classifier

doi:10.1002/int.20141

2006-01-01

PRE

Jiří Vomlel *

DOI：10.1002/int.20141

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摘要

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I discuss an application of a family of Bayesian network models-known as models of independence of causal influence (ICI)-to classification tasks with large numbers of attributes. An example of such a task is categorization of text documents, in which attributes are single words from the documents. The key that enabled application of the ICI models is their compact representation using a hidden variable. The issue of learning these classifiers by a computationally efficient implementation of the EM algorithm is addressed. Special attention is paid to the noisy-or model-probably the best-known example of an ICI model. The classification using the noisy-or model corresponds to a statistical method known as logistic discrimination. The correspondence is described. Tests of the noisy-or classifier on the Reuters data set show that, despite its simplicity, it has a competitive performance. (c) 2006 Wiley Periodicals, Inc.

Keyword:

EM ALGORITHM