Abstract:
Resume selection and classification is a very important function of Human Resource Department of every institution. Due to
increased use of technology and online job application, this department receives large volumes of resumes which has made resume
selection and classification a complex process in terms of information processing, time taken and transparency in the selection process.
In this research, a machine learning model is proposed to assist resume selection and classification. Naïve Bayes model was developed
to select and classify resumes. The predictive accuracy attained will be recorded and compared to predictive accuracy of homogeneous
Ensemble classifier model developed by using different data sets. Naïve Bayes classifier models obtained from different data sets was
used as base classifiers to develop Ensemble Naïve Bayes Classifier. It was observed that the new model produced a better predictive
accuracy compared to the original Naïve Bayes Classifier model. The original Naïve Bayes classifier model gave an average predictive
accuracy of 89.8148% while Ensemble Naïve Bayes Classifier model attained an overall accuracy of 94.4444%.
