Theory and Applications of Attribute Decomposition

Authors:
Lior Rokach;Oded Mainon
Affiliations:
-;-
Venue:
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Year:
2001

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Abstract

This paper examines the Attribute Decomposition Approach with simple Bayesian combination for dealing with classi拢cation problems that contain high number ofattributes and moderate numbers of records. According to the attribute Decomposition approach, the set of input attributes is automatically decomposed into several subsets. classi拢cation model is built for each subset, then all the models are combined using simple Bayesian combination.This paper presents theoretical and practical foundation for the Attribute Decomposition approach. A greedyprocedure, called D-IFN, is developed to decompose the input attributes set into subsets and build a classi拢cation model for each subset separately. The results achieved in theempirical comparison testing with well-known classi拢cationmethods (like C4.5)indicate the superiority of the decomposition approach.