文章基本信息

标题：Adaboost Ensemble with Simple Genetic Algorithm for Student Prediction Model
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作者：AhmedSharaf ElDen ; Malaka A. Moustafa ; Hany M. Harb 等
期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2013
卷号：5
期号：2
页码：73
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Predicting the student performance is a great concern to the higher education managements.Thisprediction helps to identify and to improve students' performance.Several factors may improve thisperformance.In the present study, we employ the data mining processes, particularly classification, toenhance the quality of the higher educational system. Recently, a new direction is used for the improvementof the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearningalgorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the geneticalgorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,especially in very large classes. This early prediction allows the instructor to provide appropriate advisingto those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the resultsshowed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces thecomplexity of computation.
关键词：Data mining; AdaBoost; Genetic Algorithm; Feature Selection; Predictive Model;ASSISTments Platform;datase