混合数据中贝叶斯网络学习的多元离散化方法
A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
摘要：
在本文中，我们解决了离散化的背景下学习贝叶斯网络（BN）的数据包含连续和离散变量的问题。我们描述了一种新的<EM>多元</EM>离散化技术，其中每个连续变量离散化，同时考虑到它与其他变量的相互作用。该技术是基于使用贝叶斯评分度量，评分的离散化政策的连续变量给定的BN结构和观察到的数据。由于该度量是相对于当前正在评估的BN结构的，因此变量的离散化需要随着BN结构的变化而动态调整。
英文摘要：
In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe a new technique for <EM>multivariate</EM> discretization, whereby each continuous variable is discretized while taking into account its interaction with the other variables. The technique is based on the use of a Bayesian scoring metric that scores the discretization policy for a continuous variable given a BN structure and the observed data. Since the metric is relative to the BN structure currently being evaluated, the discretization of a variable needs to be dynamically adjusted as the BN structure changes.
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