Proceedings 2001 IEEE International Conference on Data Mining (2001)
San Jose, California
Nov. 29, 2001 to Dec. 2, 2001
This paper examines the Attribute Decomposition Approach with simple Bayesian combination for dealing with classi£cation problems that contain high number of attributes and moderate numbers of records. According to the attribute Decomposition approach, the set of input attributes is automatically decomposed into several subsets. classi£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 greedy procedure, called D-IFN, is developed to decompose the input attributes set into subsets and build a classi£cation model for each subset separately. The results achieved in the empirical comparison testing with well-known classi£cation methods (like C4.5)indicate the superiority of the decomposition approach.
O. Mainon and L. Rokach, "Theory and Applications of Attribute Decomposition," Proceedings 2001 IEEE International Conference on Data Mining(ICDM), San Jose, California, 2001, pp. 473.