东华大学学报（英文版）2006，Vol.23Issue(2)：74-79,6.
Support Vector Machine Ensemble Based on Genetic Algorithm
Support Vector Machine Ensemble Based on Genetic Algorithm
摘要
Abstract
Support vector machines (SVMs) have been introduced as effective methods for solving classification problems.However, due to some limitations in practical applications,their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE.Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs,bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.关键词
ensemble learning/genetic algorithm/support vector machine/diversityKey words
ensemble learning/genetic algorithm/support vector machine/diversity分类
数理科学引用本文复制引用
LI Ye,YIN Ru-po,CAI Yun-ze,XU Xiao-ming..Support Vector Machine Ensemble Based on Genetic Algorithm[J].东华大学学报（英文版）,2006,23(2):74-79,6.基金项目
This work was supported by National Basic Research Program of China under Grant 2002cb312200-01-3 and National Nature Science Foundation of China under Grant 60174038. （）
