期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2014
卷号：4
期号：5
页码：1
DOI：10.5121/ijdkp.2014.4501
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：A high prediction accuracy of the students’ performance is more helpful to identify the low performancestudents at the beginning of the learning process. Data mining is used to attain this objective. Data miningtechniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve theclassification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used forthe binary classification and not applicable to multiclass classification directly. SAMME boostingtechnique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system using Multi Agent Data Mining is proposed topredict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. Theresults show that using SAMME Boosting technique improves the prediction accuracy and outperformedC4.5 single classifier and LogitBoost.
关键词：E-learning; Educational Data Mining; Classification; Ensemble of Classifier; Boosting; Adaboost; SAMME;Adaboost.M1; LogitBoost; Students’ Performance Prediction; Multi-Agent System.
