In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe ...
Abstract. In Machine Learning, ensembles are combination of classifiers. Their objective is to improve the accuracy. In previous works, we have presented a method for the generati...
Abstract Supervised classification is one of the tasks most frequently carried out by socalled Intelligent Systems. Thus, a large number of techniques have been developed based on ...
Sotiris B. Kotsiantis, Ioannis D. Zaharakis, Panay...
Algorithms for learning the conditional probabilities of Bayesian networks with hidden variables typically operate within a high-dimensional search space and yield only locally op...
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the E...