Xiannian Fan

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A recent breadth-first branch and bound algorithm (BF-BnB) for learning Bayesian network structures (Mal-one et al. 2011) uses two bounds to prune the search space for better efficiency; one is a lower bound calculated from pattern database heuristics, and the other is an upper bound obtained by a hill climbing search. Whenever the lower bound of a search(More)
Several recent algorithms for learning Bayesian network structures first calculate potentially optimal parent sets (POPS) for all variables and then use various optimization techniques to find a set of POPS, one for each variable, that constitutes an optimal network structure. This paper makes the observation that there is useful information implicit in the(More)
This is a preview version of the paper [1] (see page 14 for the reference). Abstract. Learning from imbalanced datasets has drawn more and more attentions from both theoretical and practical aspects. Over-sampling is a popular and simple method for imbalanced learning. In this paper, we show that there is an inherently potential risk associated with the(More)
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