Symmetric Causal Independence Models for Classification
Rasa Jurgelenaite, Tom Heskes · 2006
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we propose an application of an extended class of causal independence models, causal independence models based on the symmetric Boolean function, for classification. We present an EM algorithm to learn the parameters of these models, and study convergence of the algorithm. Experimental results on the Reuters data collection show the competitive classification performance of causal independence models based on the symmetric Boolean function in comparison to noisy OR model and, consequently, with other state-of-the-art classifiers. 1