An adaptive personnel selection model for recruitment using domain-driven data mining

Abstract

To support organizations in structuring personnel selection strategy for recruitment, various researches have been conducted using data mining approaches, and selection models containing selection rules were developed. Based on the methodology used, researches conducted were categorized as method–driven and domain–driven data mining approach of which domain–driven was discovered the preferred due to its model applicability in the real world. However, with the occasional changes in organization selection strategy, the models developed cannot adapt to these changes due to the static nature of the rules contained in the models. This research aims at developing an adaptive personnel selection model to support personnel selection for recruitment and adapt to the changes in personnel selection strategy. The framework used in developing the model involves Federal University Lokoja Nigeria recruitment dataset usage for extraction of selection rules to support personnel selection process using decision tree method of classification, generation of adaptive rules to handle the changes in personnel selection strategy using frequent and non-frequent pattern of data mining and domain expert’s validation of each rule developed. The result of the implementation of the proposed model was ranked the highest after comparing it with selection models developed using four decision trees.