@article{(Open Science Index):https://publications.waset.org/pdf/10287,
	  title     = {Improving Academic Performance Prediction using Voting Technique in Data Mining},
	  author    = {Ikmal Hisyam Mohamad Paris and  Lilly Suriani Affendey and  Norwati Mustapha},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {4},
	  number    = {2},
	  year      = {2010},
	  pages     = {306 - 309},
	  ee        = {https://publications.waset.org/pdf/10287},
	  url   	= {https://publications.waset.org/vol/38},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 38, 2010},