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IJSTR >> Volume 8 - Issue 7, July 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

A Novel Hybrid Approach Of Adaboostm2 Algorithm And Differential Evolution For Prediction Of Student Performance

[Full Text]



Samuel-Soma M. Ajibade, Nor Bahiah Binti Ahmad, Siti Mariyam Shamsuddin



AdaBoost, AdaBoostM2, Classification techniques, Data mining, Differential Evolution, prediction model, Student Performance



The prediction of performance of student is a very important task for institutions of higher learning. The academic performance of students aids the teachers, instructors and management of institutions to identify low performing students and then more attention is given to them so as to enhance their performances. In previous studies, various elements and methods have been applied to identify and enhance the performances of students. In this work, we made use of data mining classifcation techniques to improve the prediction accuracy of student performance. In recent times, classification accuracy has been enhanced thru the use of ensemble techniques and combining multiple classifiers. In this paper, we have made use of an efficient AdaBoost ensemble technique called AdaBoostM2 and we combined it with a metaheuristic optimization algorithm known as Differential Evolution (DE) to produce a novel algorithm called “ADDE”. This new algorithm is implemented on the KalBoard 360 educational dataset and the results displays that is very efficient in reducing the weak learners and thereby increases the prediction accuracy. The new algorithm has therefore shown better result in reducing the computation complexity.



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