Theory and applications of attribute decomposition
- 14 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 473-480
- https://doi.org/10.1109/icdm.2001.989554
Abstract
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.Keywords
This publication has 8 references indexed in Scilit:
- Data Mining by Attribute Decomposition with Semiconductor Manufacturing Case StudyPublished by Springer Nature ,2001
- Feature transformation by function decompositionIEEE Intelligent Systems and their Applications, 1998
- Feature Selection for Knowledge Discovery and Data MiningPublished by Springer Nature ,1998
- On the Optimality of the Simple Bayesian Classifier under Zero-One LossMachine Learning, 1997
- Error reduction through learning multiple descriptionsMachine Learning, 1996
- The Nature of Statistical Learning TheoryPublished by Springer Nature ,1995
- Principal Components AnalysisPublished by SAGE Publications ,1989
- A Projection Pursuit Algorithm for Exploratory Data AnalysisIEEE Transactions on Computers, 1974