Development of New Hybrid Admission Decision Prediction Models Using Support Vector Machines Combined with Feature Selection
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Abstract
The purpose of this paper is to develop new hybrid admission decision prediction models by using Support Vector Machines (SVM) combined with a feature selection algorithm to investigate the effect of the predictor variables on the admission decision of a candidate to the School of of Physical Education and Sports at Cukurova University. Experiments have been conducted on the dataset, which contains data of participants who applied to the School in 2006. The dataset has been randomly split into training and test sets using 10-fold cross validation as well as different percentage ratios. The performance of the prediction models for the datasets has been assessed using classification accuracy, specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV). The results show that a decrease in the number of predictor variables in the prediction models usually leads to a parallel decrease in classification accuracy.
Keywords: machine learning; prediction; physical ability test; feature selection;
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