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Multi-relational decision tree induction

机译:多关系决策树归纳

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Discovering decision trees is an important set of techniques in KDD, both because of thieir simple interpretation and the efficiency of their discovery.One disadvantage is that they do not take the structure of the data into account.By going from the standard single-relation approach to the multi-relational approach as in ILP this disadvantage is removed.however,the straightformward generalisation loses the efficiency.In this papae rwe present a framework that allows for efficient discovery of multi-relational decision trees through exploitation of domain knowledge encoded in the data model of the database.
机译:无论是因为解释简单还是发现效率高,发现决策树都是KDD中的重要技术集之一,缺点是它们没有考虑数据的结构,这是通过标准的单关系方法来实现的。对于ILP中的多关系方法,此缺点已消除。但是,直接形式化的通用性降低了效率。在本论文中,我们提出了一个框架,该框架允许通过利用数据中编码的领域知识来有效发现多关系决策树。数据库的模型。

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