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Noisy‐or classifier
Author(s) -
Vomlel Jiří
Publication year - 2006
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20141
Subject(s) - computer science , classifier (uml) , artificial intelligence , machine learning , categorization , naive bayes classifier , pattern recognition (psychology) , support vector machine
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. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 381–398, 2006.