文献类型：会议论文

英文题名：Research on the Classification of High Dimensional Imbalanced Data Based on the Optimizational Random Forest Algorithm

作者：Bo, Su

第一作者：Bo, Su

通信作者：Bo, S[1]|[144402a78eb4004052ca8]苏博;

机构：[1]Guizhou Inst Technol, Guiyang 550025, Guizhou, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Inst Technol, Guiyang 550025, Guizhou, Peoples R China.|贵州理工学院;

会议论文集：9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

会议日期：JAN 14-15, 2017

会议地点：Changsha, PEOPLES R CHINA

语种：英文

外文关键词：Random forest algorithm; High dimension; Unbalanced data; Classification

年份：2017

摘要：The random forest algorithm is a new classification and prediction model algorithm. So far, there is not much research on the problem of unbalanced data for random forest classification, ditto, no direct and effective method. On the basis of feature selection algorithm based on correlation measure, the integration feature selection method was helpful to increase the selection probability of classification feature of positive class samples, which used the subspace selection algorithm based on stratified sampling, sampling the feature subset generated by the integration feature selection method, respectively, which ensured the importance of the feature and the difference of the generated model. The simulation results show that the proposed method is effective.