PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discriminative learning of Bayesian networks via factorized conditional log-likelihood
Alexandra Carvalho, Teemu Roos, Arlindo Oliveira and Petri Myllymäki
Journal of Machine Learning Research Volume 12, pp. 2181-2210, 2011.

Abstract

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9127
Deposited By:Petri Myllymäki
Deposited On:21 February 2012