TY - GEN
T1 - Theory and applications of attribute decomposition
AU - Rokach, Lior
AU - Mainon, Oded
PY - 2001/12/1
Y1 - 2001/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=56349143572&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:56349143572
SN - 0769511198
SN - 9780769511191
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 473
EP - 480
BT - Proceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
T2 - 1st IEEE International Conference on Data Mining, ICDM'01
Y2 - 29 November 2001 through 2 December 2001
ER -