基于最小描述长度的贝叶斯网络属性聚集与离散化搜索策略
An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length
摘要：
贝叶斯网络是表示大量随机变量之间的复杂关系的高维概率分布的方便的图形表达。它们被广泛应用于生物信息学、人工智能、诊断和风险管理等领域。从数据中恢复网络的结构对于建模、分析和预测是至关重要的。文献中的大多数恢复算法假设连续但高斯的离散数据。对于一般的连续数据，通常采用离散化，但往往会破坏要恢复的结构。Friedman和Goldszweek提出了一种基于最小描述长度原则的方法，该方法选择了保留原始数据集中信息的离散化，但是即使对于中等规模的网络，也很难实现。在本文中，我们提供了一个非常有效的搜索策略，它允许人们在实践中使用的弗里德曼和Goldszerosis离散化。
英文摘要：
Bayesian networks are convenient graphical expressions for high dimensional probability distributions representing complex relationships between a large number of random variables. They have been employed extensively in areas such as bioinformatics, artificial intelligence, diagnosis, and risk management. The recovery of the structure of a network from data is of prime importance for the purposes of modeling, analysis, and prediction. Most recovery algorithms in the literature assume either discrete of continuous but Gaussian data. For general continuous data, discretization is usually employed but often destroys the very structure one is out to recover. Friedman and Goldszmidt suggest an approach based on the minimum description length principle that chooses a discretization which preserves the information in the original data set, however it is one which is difficult, if not impossible, to implement for even moderately sized networks. In this paper we provide an extremely efficient search strategy which allows one to use the Friedman and Goldszmidt discretization in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
