Graduates Employability Prediction through Rule-Based Classification Techniques with SMOTE in Imbalanced Data Sets
Keywords:
graduates employability, rule-based classification, SMOTEAbstract
This research aims to develop graduates’ employability prediction models through rule-based classification techniques with SMOTE in imbalanced data sets. After analyzing the data, a class imbalance problem was found. In order to improve the quality of the data, SMOTE was used to increase the minority class. Then rules-based classification techniques (RIPPER, PART, and PRISM) were used to build the prediction models. Moreover, 5-fold cross-validation was utilized to split the data into the training and test sets. This research has measured performance models with accuracy, precision, recall, and f-measure. The experimental results demonstrated that the PRISM algorithm combined with SMOTE had the highest efficiency, with an accuracy of 85.69%, precision of 85.60%, recall of 85.70%, and f-measure of 85.60%, respectively.
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