Academic performance prediction based on voting technique

Autor:	Muhammad Sufyian Mohd Azmi, Ikmal Hisyam Bin Mohamad Paris
Rok vydání:	2011
Předmět:	Computer science business.industry media_common.quotation_subject Machine learning computer.software_genre Class (biology) Data set Voting Classifier (linguistics) ComputingMilieux_COMPUTERSANDEDUCATION Performance prediction Artificial intelligence Remedial education business computer media_common
Zdroj:	2011 IEEE 3rd International Conference on Communication Software and Networks.
DOI:	10.1109/iccsn.2011.6014841
Popis:	Student's grade has always been critical issues that occur quite often in universities providing high learning education. Currently there are many techniques to predict student's grade. In this paper we compare the accuracy of data mining methods to classifying students in order to predicting student's class grade. These predictions are more useful for identifying weak students and assisting management to take remedial measures at early stages to produce excellent graduate that will graduate at least with second class upper. Firstly we examine single classifiers accuracy on our data set and choose the best one and then ensembles it with a weak classifier to produce simple voting method. We present results show that combining different classifiers outperformed other single classifiers for predicting student performance.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f69c280605580c70272449c238641e6b https://doi.org/10.1109/iccsn.2011.6014841 Zobrazit plný text záznamu