Pattern-based decision tree construction

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Pattern-based decision tree construction
Learning classifiers has been studied extensively the last two decades. Recently, various approaches based on patterns (e.g., association rules) that hold within labeled data have been considered. In this paper, we propose a novel associative classification algorithm that combines rules and a decision tree structure. In a so-called δ-PDT (δ-Pattern Decision Tree), nodes are made of selected disjunctive δstrong classification rules. Such rules are generated from collections of δ-free patterns that can be computed efficiently. These rules have a minimal body, they are nonredundant and they avoid classification conflicts under a sensible condition on δ. We show that they also capture the discriminative power of emerging patterns. Our approach is empirically evaluated by means of a comparison to stateof-the-art proposals.
Dominique Gay, Nazha Selmaoui, Jean-Françoi
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Authors Dominique Gay, Nazha Selmaoui, Jean-François Boulicaut
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