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An Online Education Data Classification Model Based on Tr_MAdaBoost Algorithm

YU Lasheng  WU Xu  YANG Yu  
【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 Tr_MAdaBoost. 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.

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中国期刊全文数据库 前2条
1 YU Lasheng;WU Xu;YANG Yu;;An Online Education Data Classification Model Based on Tr_MAdaBoost Algorithm[J];Chinese Journal of Electronics;2019年01期
2 ;Supporting Education[J];China & Africa;2012年05期
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