SUPPORT VECTOR MACHINES ENSEMBLE WITH OPTIMIZING WEIGHTS BY GENETIC ALGORITHM

摘要：

Support Vector Machines (SVM) is a classification technique based on the structural risk minimization principle.It is characteristic of processing complex data and high accuracy. And the ensemble of classifiers often has better performance than any of component classifiers in the ensemble. In this paper, bagging, boosting, multiple SVM decision model (MSDM) and heterogeneous SVM decision model (HSDM) of SVM ensemble are compared on four data sets. For boosting and bagging, genetic algorithm is used to optimize the combining weights of component SVMs.Experiment results show that SVM ensemble with optimizing weights by genetic algorithm could gain higher accuracy.

关键词： Support vector machines genetic algorithm ensemble classification

作者: LING-MIN HE XIAO-BING YANG FAN-SHENG KONG

作者单位: College of Information Engineering, China Jiliang University, Hangzhou 310018, China;Artificial Inte College of Information Engineering, China Jiliang University, Hangzhou 310018, China Artificial Intelligence Institute, Zhejiang University, Hangzhou 310027,China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3503-3507

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）