文献类型：会议论文

英文题名：A Preprocessing Method of AdaBoost for Mislabeled Data Classification

作者：Liu, Xiangyang[1];Dai, Yaping[1];Zhang, Yan[1];Yuan, Qiao[2];Zhao, Linhui[3]

第一作者：Liu, Xiangyang

通讯作者：Liu, XY[1]

机构：[1]Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China;[2]Shandong Zaozhuang Expt High Sch, Class Grade 1 2, Zaozhuang 277800, Peoples R China;[3]Beijing Union Univ, Coll Mech & Elect Engn, Beijing 100020, Peoples R China

第一机构：Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China

通讯机构：[1]corresponding author), Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China.

会议论文集：29th Chinese Control And Decision Conference (CCDC)

会议日期：MAY 28-30, 2017

会议地点：Chongqing, PEOPLES R CHINA

语种：英文

外文关键词：AdaBoost; Classification; Mislabeled data; preprocessing method; decision stump

摘要：AdaBoost is one of the most popular algorithm for classification and has been successfully used for text classification, face detection and tracking. However noise sensitivity is regarded as a major disadvantage and previous works show that AdaBoost will be overfitting when dealing with the data sets with noisy data. To improve the noise tolerance of conventional AdaBoost, this paper proposed a preprocessing method of AdaBoost for mislabeled data to find the noisy data and correct it. Further decision stump is selected as the weak learner of the AdaBoost algorithm for classification. The comparison of simulation results between conventional AdaBoost and the method proposed in this paper shows that the proposed algorithm has improved testing accuracy of the data sets with the noisy data.