Title :
Z splitting criterion for growing trees and boosting
Author_Institution :
Monmouth Univ., West Long Branch, NJ, USA
Abstract :
A splitting criterion that arrives out of the context of a new boosting algorithm is used to construct classification trees. Trees constructed using this Z function are compared to those using the entropy function of C4.5 and are found to give much lower error rates. The Z function is also used to construct boosting machines which, when compared to other implementations, give lower error rates
Keywords :
learning (artificial intelligence); neural nets; pattern classification; trees (mathematics); C4.5; Z splitting criterion; boosting machines; classification trees; error rates; learning algorithm; pattern classification; pruning; Boosting; Classification tree analysis; Entropy; Equations; Error analysis;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831141
