Theory and applications of attribute decomposition

• Published in: Proceedings 2001 IEEE International Conference on Data Mining pp. 473 - 480
• Main Authors: Rokach, L., Mainon, O.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2001
• Subjects: Bayesian methods; Data mining; Data visualization; Humans; Industrial engineering; Large-scale systems; Predictive models; Principal component analysis; Testing
• ISBN: 9780769511191; 0769511198
• DOI: 10.1109/ICDM.2001.989554

This paper examines the attribute decomposition approach with simple Bayesian combination for dealing with classification 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. A classification 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 classification model for each subset separately. The results achieved in the empirical compart. son testing with well-known classification methods (like C4.5) indicate the superiority of the decomposition approach.