Multivariate Cluster-Based Discretization for Bayesian Network Structure Learning
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While there exist many efficient algorithms in the literature for learning Bayesian networks with discrete random variables, learning when some variables are discrete and others are continuous is still an issue. A common way to tackle this problem is to preprocess datasets by first discretizing continuous variables and, then, resorting to classical discrete variable-based learning algorithms. However, such a method is inefficient because the conditional dependences/arcs learnt during the learning phase bring valuable information that cannot be exploited by the discretization algorithm, thereby preventing it to be fully effective In this paper, we advocate to discretize while learning and we propose a new multivariate discretization algorithm that takes into account all the conditional dependences/arcs learnt so far. Unlike popular discretization methods, ours does not rely on entropy but on clustering using an EM scheme based on a Gaussian mixture model. Experiments show that our method significantly outperforms the state-of-the-art algorithms.
KeywordsMultivariate discretization Bayesian network learning
This work was supported by the French Institute for Radioprotection and Nuclear Safety (IRSN), the Belgium’s nuclear safety authorities (Bel V) and European project H2020-ICT-2014-1 #644425 Scissor.
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