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Development of New Hybrid Admission Decision Prediction Models Using Support Vector Machines Combined with Feature Selection
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Published:
Mar 22, 2017
Updated:
2017-03-22
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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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How to Cite
Ozsert Yıgıt, G., Akay, M. F., & Alak, H. (2017). Development of New Hybrid Admission Decision Prediction Models Using Support Vector Machines Combined with Feature Selection. New Trends and Issues Proceedings on Humanities and Social Sciences, 3(3), 1–10. https://doi.org/10.18844/prosoc.v3i3.1502
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References
Abut, F., Akay, M. F., Turhan, I. & Ozsert, G. (2015). Performance evaluation of different classifiers for predicting the admission decision of a candidate to the school of physical education and sports at Cukurova University. In Proceedings of the 1st International Symposium on Sport Science, Engineering and Technology (ISSSET2015), Istanbul, 10-13 May 2015, 178-184.
Acikkar, M. & Akay, M. F. (2009). Support vector machines for predicting the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Expert Systems with Applications, 36 (3), 7228-7233.
Acikkar, M., Akay, M. F. Abut, F. & Isoglu, O. (2014). Predicting the admission decision of a candidate to the school of physical education and sports at Cukurova University by using Multilayer Perceptron combined with feature selection. In Proceedings of the Second International Symposium on Engineering, Artificial Intelligence & Applications (ISEAIA2014), North Cyprus, 5-7 Nov 2014, 15-16.
Akay, M. F., Guler, M. & Acikkar, M. (2014). Multilayer perceptron models for predicting the admission decision of a candidate to the school of physical education and sports at Cukurova University. In Proceedings of the Second International Symposium on Engineering, Artificial Intelligence & Applications (ISEAIA2014), North Cyprus, 5-7 Nov 2014, 10.
Ozsert-Yigit, G., Akay, M.F. & Alak, H. (2017). Development of New Hybrid Admission Decision Prediction Models Using Support Vector
Machines Combined with Feature Selection. New Trends and Issues Proceedings on Humanities and Social Sciences. [Online]. 03, pp 01-10.
Available from: www.prosoc.eu
10
Jaganathan, P., Rajkumar, N. & Kuppuchamy, R. (2012). A comparative study of improved F-Score with Support Vector Machine and RBF network for breast cancer classification. International Journal of Machine Learning and Computing, 2 (6), 741-745.
Retrieved from http://besyo.cu.edu.tr
Retrieved from http://besyo.cu.edu.tr/besyo/TR/OzelYetenekSinavi.aspx
Sun, Y., Lou, X. & Bao, B. (2011). A novel Relief feature selection algorithm based on Mean-Variance model. Journal of Information & Computational Science, 8 (16), 3921-3929.
Witten, I. H. & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Xu, H., Lemischka, I. R. & Ma'ayan, A. (2010). SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells. BMC systems biology, 4 (1), 1.
Young, J., Ridgway, G., Leung, K. K., Barnes, J. & Ourselin, S. (2011). Prediction of MCI to Alzheimer's conversion with hippocampal shape features and support vector machine. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 7 (4), 41.
Acikkar, M. & Akay, M. F. (2009). Support vector machines for predicting the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Expert Systems with Applications, 36 (3), 7228-7233.
Acikkar, M., Akay, M. F. Abut, F. & Isoglu, O. (2014). Predicting the admission decision of a candidate to the school of physical education and sports at Cukurova University by using Multilayer Perceptron combined with feature selection. In Proceedings of the Second International Symposium on Engineering, Artificial Intelligence & Applications (ISEAIA2014), North Cyprus, 5-7 Nov 2014, 15-16.
Akay, M. F., Guler, M. & Acikkar, M. (2014). Multilayer perceptron models for predicting the admission decision of a candidate to the school of physical education and sports at Cukurova University. In Proceedings of the Second International Symposium on Engineering, Artificial Intelligence & Applications (ISEAIA2014), North Cyprus, 5-7 Nov 2014, 10.
Ozsert-Yigit, G., Akay, M.F. & Alak, H. (2017). Development of New Hybrid Admission Decision Prediction Models Using Support Vector
Machines Combined with Feature Selection. New Trends and Issues Proceedings on Humanities and Social Sciences. [Online]. 03, pp 01-10.
Available from: www.prosoc.eu
10
Jaganathan, P., Rajkumar, N. & Kuppuchamy, R. (2012). A comparative study of improved F-Score with Support Vector Machine and RBF network for breast cancer classification. International Journal of Machine Learning and Computing, 2 (6), 741-745.
Retrieved from http://besyo.cu.edu.tr
Retrieved from http://besyo.cu.edu.tr/besyo/TR/OzelYetenekSinavi.aspx
Sun, Y., Lou, X. & Bao, B. (2011). A novel Relief feature selection algorithm based on Mean-Variance model. Journal of Information & Computational Science, 8 (16), 3921-3929.
Witten, I. H. & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Xu, H., Lemischka, I. R. & Ma'ayan, A. (2010). SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells. BMC systems biology, 4 (1), 1.
Young, J., Ridgway, G., Leung, K. K., Barnes, J. & Ourselin, S. (2011). Prediction of MCI to Alzheimer's conversion with hippocampal shape features and support vector machine. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 7 (4), 41.