FEATURE SELECTON BASED ON MUTUAL INFORMATION FOR GEAR IMBALANCED PROBLEM FAULY DIAGNOSIS

摘要：

Defect is one of the important factors resulting in gear fault, so it is significant to study the technology of defect diagnosis for gear. Class imbalance problem is encountered in the fault diagnosis, which causes seriously negative effect on the performance of classifiers that assume a balanced distribution of classes. Though it is critical, few previous works paid attention to this class imbalance problem in the fault diagnosis of gear. In imbalanced problems, some features are redundant and even irrelevant. These features will hurt the generalization performance of learning machines. Here we propose MIEE (Mutual Information based feature selection for EasyEnsemble) to solve the class imbalanced problem in the fault diagnosis of gear. Experimental results on UCI data sets and gear data set show that MIEE improves the classification performance and prediction ability on the imbalanced dataset.

关键词： feature selection mutual iniformation imbalanced dataset

作者: T.Y.Liu

作者单位: School of Electric Shanghai Dianji University Shanghai, China

会议类型: 国际会议

会议名称: 2012 International Conference on System Simulation(2012年国际系统仿真学术会议)

会议地点: 上海

会议语种:英文

页码: 97-100

在线出版日期: 2012-04-06（万方平台首次上网日期，不代表论文的发表时间）