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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)

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)

Several heuristic search algorithms such as A* and breadth-first branch and bound have been developed for learning Bayesian network structures that optimize a scoring function. These algorithms rely on a lower bound function called static k-cycle conflict heuristic in guiding the search to explore the most promising search spaces. The heuristic takes as… (More)

- Xiannian Fan, Ke Tang
- FSKD
- 2010

- Xiannian Fan, Changhe Yuan
- 2014

Bayesian networks are widely used graphical models which represent uncertainty relations between the random variables in a domain compactly and intuitively. The first step of applying Bayesian networks to real-word problems typically requires building the structure of the networks. Among others, score-based exact structure learning has become an active… (More)

- Xiannian Fan, Changhe Yuan
- 2015

Bayesian networks are widely used graphical models which represent uncertain relations between the random variables in a domain compactly and intuitively. The first step of applying Bayesian networks to real-word problems typically requires building the network structure. Among others, optimal structure learning via score-and-search has become an active… (More)

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