TY - GEN
T1 - Investigation of diversity strategies in SVM ensemble learning
AU - Yu, Lean
AU - Wang, Shouyang
AU - Lai, Kin Keung
PY - 2008
Y1 - 2008
N2 - In SVM ensemble learning, diversity strategy is one of the most important determinants to obtain good performance. In order to examine and analyze the impacts of diversity strategies on SVM ensemble learning, this study tries to make such a deep investigation by taking credit scoring as an illustrative example. Experimental results found that the accuracy of ensemble models will be increased if ensemble members are carefully selected for diversity maximization. © 2008 IEEE.
AB - In SVM ensemble learning, diversity strategy is one of the most important determinants to obtain good performance. In order to examine and analyze the impacts of diversity strategies on SVM ensemble learning, this study tries to make such a deep investigation by taking credit scoring as an illustrative example. Experimental results found that the accuracy of ensemble models will be increased if ensemble members are carefully selected for diversity maximization. © 2008 IEEE.
KW - Credit scoring
KW - Diversity strategy
KW - Ensemble learning
KW - Group decision making
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=57649188058&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-57649188058&origin=recordpage
U2 - 10.1109/ICNC.2008.340
DO - 10.1109/ICNC.2008.340
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9780769533049
VL - 7
SP - 39
EP - 42
BT - Proceedings - 4th International Conference on Natural Computation, ICNC 2008
T2 - 4th International Conference on Natural Computation, ICNC 2008
Y2 - 18 October 2008 through 20 October 2008
ER -