Factor Selection for Improving Vocational Certificate Course Selection Model Performance using Wrapper Method
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Abstract
The objective of this research is to relevant select factors for improving of the vocational certificate program selection models of third secondary education students. In this research paper, data were collected from students who graduated with a vocational certificate from the Sarisongkram Industrial Technology College, Nakhon Phanom University. The data are from 2012 - 2018 academic year, total 926 records. Wrapper with the Random Tree and Support Vector Machines and Naive Bayes and were used to select the relevant factors. Technique Bagging technique, K-Nearest neighbors, Decision Tree C4.5 technique, Random Forest technique and Artificial Neural techniques were used to build the prediction models. In order to measure performance of factors, 10-fold cross validation was employed and measured with Accuracy, Sensitivity and Specifi city values. The experimental results showed that the wrapper method in combination with Random Tree was able to increase the accuracy of vocational certificate program selection model up to 89.13 %, sensitivity up to 75.86 % and specificity up to 92.86 %.
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