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- Xiannian Fan, Ke Tang, Thomas Weise
- PAKDD
- 2011

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)

- Xiannian Fan, Changhe Yuan, Brandon M. Malone
- AAAI
- 2014

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)

- Xiannian Fan, Brandon M. Malone, Changhe Yuan
- UAI
- 2014

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)

- Xiannian Fan, Changhe Yuan
- AAAI
- 2015

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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