An Online Education Data Classification Model Based on TrMAdaBoost Algorithm

作     者：YU Lasheng WU Xu YANG Yu YU Lasheng;WU Xu;YANG Yu

作者机构：School of Information Science & EngineeringCentral South University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2019年第28卷第1期

页      面：21-28页

核心收录：

学科分类：12[管理学] 0401[教育学-教育学] 04[教育学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 040110[教育学-教育技术学（可授教育学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Transfer learning Online education OEDCF AdaBoost algorithm Tr_MAdaBoost algorithm 

摘      要：With the rapid development of network information technology and the wide application of smart phones, tablet PCs and other mobile terminals, online education plays an increasingly important role in social life. This article focuses on mining useful data from the massive online education data, by using transfer learning, relying on Hadoop, to construct Online education data classification framework(OEDCF), and design an algorithm TrAdaBoost. This algorithm overcomes the traditional classification algorithms in which the required data must be restricted to independent and identically distributed data, since online education using this new algorithm can achieve the correct classification even it has different data distribution. At the same time, with the help of Hadoop’s parallel processing architecture, OEDCF can greatly enhance the efficiency of data processing, create favorable conditions for learning analysis, and promote personalized learning and other activities of big data era.