Study of Selective Ensemble Learning Method and Its Diversity Based on Decision Tree and Neural Network
Diversity among base classifiers is known to be a necessary condition for improving ensemble learning performance. In this paper, methods of selective ensemble learning including hill-climbing selection, ensemble forward sequential selection, ensemble backward sequential selection and clustering selection are studied. To measure the diversity among base classifiers in ensemble learning, the entropy E is selected as measuring method of diversity. The results of experiment show that classifiers which have the highest diversity are obtained using selective methods, and the ensemble performance is superior to the best single classifier. In addition, the classifiers selected by clustering selective technology also have the above characteristics, and the changes of the diversity are smaller when the accuracy has smaller fluctuations. Meanwhile, the number of clusters also impacts on he ensemble performance.
Diversity Generalization Performance Decision Tree Neural Network
Kai Li Yanxia Han
School of mathematics and computer, Hebei University, Baoding 071002, China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
1310-1315
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）