An Ensemble Method of Adaptive Multiple Classifiers

摘要：

With AdaBoost being constructed, base classifiers are more and more concentrating on instances which are difficult to classify. And base classifiers are only favourite to these instances. After a base classifier is constructed, its voting weight for final decision is determined and be the same to all test instances no matter which class a test instance belongs to. Considering these problems, the paper proposes an improved AdaBoost algorithm called AMC (An Ensemble Method of Adaptive Multiple Classifiers), which gains training set with stratified weighted sampling at first and then combines base classifiers with adaptive weight. Experiment shows the AMC has a higher accuracy to the given data set and produces higher value of recall rate and precision.

作者: Hengzheng Kang Yan Yang Jintan Chen

作者单位: School of Information Science and Technology, Southwest Jiaotong University Chengdu, P.R.China School of Management, Huazhong University of Science and Technology Wuhan, P.R.China

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 215-217

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）