Decomposition methodology for classification tasks: a meta decomposer framework

Authors:
Lior Rokach
Affiliations:
Department of Information Systems Engineering, Ben-Gurion University of the Negev, P.O. Box 653, 84105, Beer-Sheva, Israel
Venue:
Pattern Analysis & Applications
Year:
2006

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Abstract

The idea of decomposition methodology for classification tasks is to break down a complex classification task into several simpler and more manageable sub-tasks that are solvable by using existing induction methods, then joining their solutions together in order to solve the original problem. In this paper we provide an overview of very popular but diverse decomposition methods and introduce a related taxonomy to categorize them. Subsequently, we suggest using this taxonomy to create a novel meta-decomposer framework to automatically select the appropriate decomposition method for a given problem. The experimental study validates the effectiveness of the proposed meta-decomposer on a set of benchmark datasets.