A learning evaluation system based on classifier fusion for E-learning
Yuanhong Wu, Xiaoqiu Tan · 2009
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 E-learning 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.