An adaptive intelligent framework for assessment & selection process in staffing task

Objectives: To enhance the performance of HR’s staffing function by providing an intelligent framework that allows convenient assessment and selection procedures. Methods: We proposed a new approach that mainly uses Data Mining (DM) and Machine Learning (ML) to develop and train an intelligent framework by learning the behavior of the staffing committee in assessing and selecting applicants for specific job requirements. It utilizes fuzzy logic to mitigate the decision uncertainty and provide an objective mechanism for filtering best-fit applicants’ profiles for the next selection phase. The proposed framework was trained on a labeled dataset consisted of (414) CVs. A 5- fold cross-validation method was used to train and evaluate the proposed framework. The highest accuracy achieved was (84%) at k=2); while the lowest accuracy achieved was (71%) at K=1. Findings: The accuracy performance is at acceptable levels and can be improved as more data involved in the training process. Keywords: Staffing; data mining; fuzzy logic; machine learning