José A. Gámez

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One important approach to learning Bayesian networks (BNs) from data uses a scoring metric to evaluate the fitness of any given candidate network for the data base, and applies a search procedure to explore the set of candidate networks. The most usual search methods are greedy hill climbing, either deterministic or stochastic, although other techniques(More)
In this work we experiment with the application of island models to Estimation of Distribution Algorithms (EDAs) in the field of combinatorial optimization. This study is motivated by the success obtained by these models when applied to other meta-heuristics (such as genetic algorithms, simulated annealing or VNS) and by the use of a compact representation(More)
Most methods for exact probability propaga­ tion in Bayesian networks do not carry out the inference directly over the network, but over a secondary structure known as a junc­ tion tree or a join tree (JT). The process of obtaining a JT is usually termed compilation. As compilation is usually viewed as a whole process; each time the network is modified, a(More)
Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable conngurations given an observed evidence. These conngurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of literals. In this paper we study how to simplify the(More)
This paper proposes a new approach to the problem of obtaining the most probable explanations given a set of observations in a Bayesian network. The method provides a set of possibilities ordered by their probabilities. The main novelties are that the level of detail of each one of the explanations is not uniform (with the idea of being as simple as(More)
This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these(More)
Abductive inference in Bayesian belief networks is the process of generating the K most probable con®gurations given an observed evidence. When we are only interested in a subset of the network's variables, this problem is called partial abductive inference. Both problems are NP-hard, and so exact computation is not always possible. This paper describes an(More)