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8 Feature subset selection is a key problem in the data-mining classification task that helps to obtain more compact and understandable models without degrading (or even improving) their performance. In this work we focus on FSS in high-dimensional datasets, that is, with a very large number of predictive attributes. In this case, standard sophisticated(More)
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)
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 u 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)
This paper deals with the problem of wrapper-based feature subset selection in classification oriented datasets with a (very) large number of attributes. In such datasets sophisticated search algorithms like beam search, branch and bound, best first, genetic algorithms, etc., become intractable in the wrapper approach due to the high number of wrapper(More)
Abductive Inference in Bayesian Networks: a review. Abstract The goal of this paper is to serve as a survey for the problem of abductive inference (or belief revision) in Bayesian networks. Thus, the problem is introduced in its two variants: total abduction (or MPE) and partial abduction (or MAP). Also, the problem is formulated in its general case, that(More)