A Learning Evaluation System Based on Classifier Fusion for E-learning

摘要：

Aiming at the problem that the accuracy of an individual classifier such as Naive Bayes (NB), is not satisfactory in the present E-learning performance evaluation system, a classifier combination system has been constructed. Classifier fusion is a process that combines a set of outputs from multiple classifiers in order to achieve a more reliable and complete decision. In this work, the application of Ordered Weighted Averaging (OWA) operator as a classifier fusion approach for online learning evaluation has been investigated to combine the decisions of four underlying individual classifiers with different approaches. Considering data which gathered from Elearning platform, the accuracy of OWA-based classifier fusion system has been compared with the individual classifiers. The experiment results show a considerable improvement of online learning evaluation accuracy.

作者: WU Yuan-hong TAN Xiao-qiu GU Shen-ming

作者单位: School of Mathematics, physics & Information Science, Zhejiang Ocean University, Zhoushan,361004, Ch School of Mathematics, physics & Information Science, Zhejiang Ocean University, Zhoushan, 361004, C

会议类型: 国际会议

会议名称: 2009 IEEE International Symposium on IT in Medicine & Education( IEEE 教育与医药信息化国际会议)

会议地点: 济南

会议语种:英文

页码: 749-752

在线出版日期: 2009-08-14（万方平台首次上网日期，不代表论文的发表时间）