Generalizations of the noisy-or model

Vomlel, Jiří

Kybernetika, Tome 51 (2015) no. 3, pp. 508-524

Résumé

In this paper, we generalize the noisy-or model. The generalizations are three-fold. First, we allow parents to be multivalued ordinal variables. Second, parents can have both positive and negative influences on their common child. Third, we describe how the suggested generalization can be extended to multivalued child variables. The major advantage of our generalizations is that they require only one parameter per parent. We suggest a model learning method and report results of experiments on the Reuters text classification data. The generalized noisy-or models achieve equal or better performance than the standard noisy-or. An important property of the noisy-or model and of its generalizations suggested in this paper is that it allows more efficient exact inference than logistic regression models do.

Détail

DOI : 10.14736/kyb-2015-3-0508

Classification : 68T30, 68T37
Keywords: Bayesian networks; noisy-or model; classification; generalized linear models