A neuro-fuzzy based agent for group decision support in applicant ranking within human resources systems

Applicant selection and ranking methods for job roles within Human Resources (HR) systems involve high levels of uncertainty. This is due to the requirement to allow for the varying opinions and preferences of the different occupation domain experts in the decision making process. Hence, there is a need to develop novel systems that will enable HR departments to determine the most important requirements criteria (experience, skills etc) for a given job, based on the preferences of different domain experts, while ensuring that the experts decisions are unbiased and correctly weighted according to their knowledge and experience. This will enable a more effective way to short list submitted candidate CVs from a large number of applicants providing a consistent and fair CV ranking policy, which can be legally justified. This paper presents a novel system using a neuro-fuzzy based agent approach for automatically determining the key skill characteristics defining each expert's preferences and ranking decisions, while handling the uncertainties and inconsistencies in group decisions of a panel of experts. The presented system automates the processes of requirements specification and applicant's ranking. Experiments have been performed within the residential care sector where the proposed system has been shown to produce ranking decisions that were relatively highly consistent with those of the human experts. ©2009 IEEE.