A multivariate discretization method for learning Bayesian networks from mixed data
作者： Gregory F. Cooper , Stefano Monti
DOI:
关键词: Metric (mathematics) 、 Multivariate statistics 、 Algorithm 、 Variable (computer science) 、 Bayesian network 、 Machine learning 、 Discretization of continuous features 、 Mathematics 、 Bayesian probability 、 Discretization 、 Artificial intelligence 、 Context (language use)
摘要: In this paper we address the problem of discretization in context learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe a new technique for multivariate discretization, whereby each variable is discretized while taking into account its interaction with other The based on use scoring metric that scores policy given BN structure observed data. Since relative to currently being evaluated, needs be dynamically adjusted as changes.
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