Learning Relational Concepts with Decision Trees

机译：通过决策树学习关系概念

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In this paper, we describe two different learning tasks for relational structures. When learning a classifier for structures, the relational structures in the training sets are classified as a whole. Contrarily, when learning a context dependent classifier for elementary objects, the elementary objects of the relational structures in the training set are classified. In general, the class of an elementary object will not only depend on its elementary properties, but also on its context, which has to be learned, too. We investigate the question how such classifications can be induced automatically from a given training set containing classified structures or classified elementary objects respectively. We present a graph theoretic algorithm that allows the description of the objects in the training set by automatically constructed attributes. This allows us to employ well-known methods of decision tree induction to construct a hypothesis. We present the system INDIGO and compare it with the LINUS-system, known in ILP. The performance of INDIGO is evaluated on the Mesh and the Mutagenicity Data - two datasets that were studied in Machine Learning literature.

机译：在本文中，我们描述了关系结构的两种不同的学习任务。当学习结构的分类器时，训练集中的关系结构被整体分类。相反，当学习基本对象的上下文相关分类器时，训练集中的关系结构的基本对象被分类。通常，基本对象的类不仅取决于其基本属性，还取决于其上下文，这也必须学习。我们研究如何从包含分类结构或分类基本对象的给定训练集中自动归纳此类分类的问题。我们提出了一种图形理论算法，该算法允许通过自动构造的属性来描述训练集中的对象。这使我们能够采用众所周知的决策树归纳方法来构建假设。我们介绍系统INDIGO并将其与ILP中已知的LINUS系统进行比较。在网格和诱变数据上评估了INDIGO的性能-这是机器学习文献中研究的两个数据集。

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《Machine learning》|1996年|166-174|共9页
会议地点 Bari(IT);Bari(IT)
作者
Peter Geibel; Fritz Wysotzki;
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作者单位

Technical University Berlin Computer Science Department D-10587 Berlin, Germany;

Technical University Berlin Computer Science Department D-10587 Berlin, Germany;

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原文格式 PDF
正文语种 eng
中图分类计算机的应用;
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Learning Relational Concepts with Decision Trees

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