TY - GEN
T1 - Side effect of cut in decision tree generation for continuous attributes
AU - WANG, Xi-Zhao
AU - GAO, Xiang-Hui
AU - HE, Qiang
N1 - This paper is supported by the Machine Learning Center of the Hebei University.
PY - 2010
Y1 - 2010
N2 - There is a phenomenon that binary decision trees generated for continuous attributes have lower prediction accuracy on near boundary examples than total testing dataset. In this paper, we propose a new approach by fuzzifying crisp rules into fuzzy IF-THEN rules and using fuzzy matching operator (V, +) to overcome this problem. Experimental results show that this method can obtain good performance.
AB - There is a phenomenon that binary decision trees generated for continuous attributes have lower prediction accuracy on near boundary examples than total testing dataset. In this paper, we propose a new approach by fuzzifying crisp rules into fuzzy IF-THEN rules and using fuzzy matching operator (V, +) to overcome this problem. Experimental results show that this method can obtain good performance.
KW - Binary decision tree
KW - Continuous attributes
KW - Cut points
KW - Side effect
UR - http://www.scopus.com/inward/record.url?scp=78751552212&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2010.5642456
DO - 10.1109/ICSMC.2010.5642456
M3 - Conference paper (refereed)
AN - SCOPUS:78751552212
SN - 9781424465866
T3 - International Conference on Systems, Man and Cybernetics
SP - 1364
EP - 1369
BT - Proceedings : 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
PB - IEEE
T2 - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
Y2 - 10 October 2010 through 13 October 2010
ER -