Fast Optimizing Parameters Algorithm for Least Squares Support Vector Machine Based on Artificial Immune Algorithm

摘要：

When Least Squares Support Vector Machine (LS-SVM) is used to classify on large datasets,training samples to get the optimal model parameters is a timeconsuming and memory consumption process. To reduce training time and computational complexity,we develop a novel algorithm for selecting LS-SVM meta-parameter values based on ideas from principle of artificial immune. By analyzing LS-SVM parameters on the classification accuracy,we find there are many parameters combinations that make the same classification accuracy;Whats more,once one of the parameters fixed and the other changes in a certain range,their combinations do not affect the classification accuracy. We regard LS-SVM parameters as antibody genes and design reasonable coding scheme for them. Then we employ artificial immune algorithm to search the optimal model parameters of LS-SVM. We provide experiments to demonstrate the performance of LS-SVM. Results show that the proposed algorithm greatly enhances parameters optimizing efficiency while keeping the approximately same classification accuracy with the some other existent methods such as multi-fold cross-validation and grid-search.

关键词： Least Squares Support Vector Machine Artificial Immune Algorithm Parameters Optimization

作者: Fugang Yang

作者单位: School of information & electronics engineering Shandong Institute of Business and Technologyi Yantai,264005

会议类型: 国际会议

会议名称: 2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

会议地点: 北京

会议语种:英文

页码: 3176-3181

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）