Theory and applications of attribute decomposition

Lior Rokach*, Oded Mainon

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations


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 comparison testing with well-known classification methods (like C4.5) indicate the superiority of the decomposition approach.

Original languageEnglish
Title of host publicationProceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
Number of pages8
StatePublished - 2001
Event1st IEEE International Conference on Data Mining, ICDM'01 - San Jose, CA, United States
Duration: 29 Nov 20012 Dec 2001

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference1st IEEE International Conference on Data Mining, ICDM'01
Country/TerritoryUnited States
CitySan Jose, CA


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