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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Data categorization using decision trellises
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Data categorization using decision trellises

机译:使用决策网格进行数据分类

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摘要

We introduce a probabilistic graphical model for supervised learning on databases with categorical attributes. The proposed belief network contains hidden variables that play a role similar to nodes in decision trees and each of their states either corresponds to a class label or to a single attribute test. As a major difference with respect to decision trees, the selection of the attribute to be tested is probabilistic. Thus, the model can be used to assess the probability that a tuple belongs to some class, given the predictive attributes. Unfolding the network along the hidden states dimension yields a trellis structure having a signal flow similar to second order connectionist networks. The network encodes context specific probabilistic independencies to reduce parametric complexity. We present a custom tailored inference algorithm and derive a learning procedure based on the expectation-maximization algorithm. We propose decision trellises as an alternative to decision trees in the context of tuple categorization in databases, which is an important step for building data mining systems. Preliminary experiments on standard machine learning databases are reported, comparing the classification accuracy of decision trellises and decision trees induced by C4.5. In particular, we show that the proposed model can offer significant advantages for sparse databases in which many predictive attributes are missing.
机译:我们介绍了一种概率图形模型,用于在具有分类属性的数据库上进行有监督的学习。所提出的信念网络包含隐藏变量,其作用类似于决策树中的节点,并且它们的每个状态要么对应于类标签,要么对应于单个属性测试。作为决策树的主要区别,要测试的属性的选择是概率性的。因此,在给定预测属性的情况下,该模型可用于评估元组属于某个类别的概率。沿隐藏状态维度展开网络会产生网格结构,该网格结构的信号流类似于二阶连接器网络。网络对上下文特定的概率独立性进行编码,以减少参数复杂性。我们提出了一种定制的定制推理算法,并基于期望最大化算法推导了学习过程。在数据库中元组分类的背景下,我们提出了决策网格作为决策树的替代方案,这是构建数据挖掘系统的重要步骤。报告了在标准机器学习数据库上的初步实验,比较了C4.5诱导的决策网格和决策树的分类准确性。特别是,我们表明,所提出的模型可以为缺少许多预测属性的稀疏数据库提供显着的优势。

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