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By the selected abstractions, the original database can be transformed into a small generalized database written in abstract values. Therefore, it would be ...
From this point of view, we propose in this paper a method of selecting an appropriate abstraction from possible ones, assuming that our task is to construct a ...
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... The Classification Tree procedure creates a tree-based model. It classifies cases into groups or predicts values of a dependent (CHRisk) variable based on ...
By the selected abstractions, the original database can be transformed into a small generalized database written in abstract values. Therefore, it would be ...
Hence, attribute-oriented induction allows the user to view the data at more meaningful abstractions. ... data, prior to decision tree induction. This ...
Jan 21, 2021 · In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 ...
The paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute oriented induction, relevance ...
Decision tree induction is extremely popular in data mining, with most currently available techniques being refinements of Quinlan's original work (Quinlan ...
The induction of decision trees is a widely-used approach to build classification models that guarantee high performance and expressiveness.
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This paper formalizes the problem of inductive learning using ontologies and data; describes an ontology-driven decision tree learning algorithm to learn ...