A STUDY ON APPLICATION OF SEMI-SUPERVISED COLLABORATIVE CLASSIFICATION ALGORITHM

摘要：

　　The treatment method of Tri-Training algorithm in classifier selection and confidence estimation breaks through the limitation of Co-training algorithm.In order to further improve the classifiers performance,a semi-supervised collaborative classification algorithm with enhanced difference makes some improvement respectively on classifier diversity,model update strategy and unlabeled sample prediction method.Because of the use of different classifiers and consideration of classifier diversity,this algorithm has good performance in unbalanced sample set classification.Establish classification model based on the above algorithm,and use it to do experiment with bridge structural health monitoring data,the results of which demonstrate the validity and applicability.

关键词： Confidence estimation Strategy of model update Imbalanced sample set Bridge structural health -monitoring data

作者: Chongchong Yu Lili Shang Li Tan Xuyan Tu Yang Yang

作者单位: School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijin School of Computer and Information Engineering,Beijing Technology and Business University,Beijing 10 School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijin

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 773-779

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）