Fuzzification Technique for Candidate Rating and Selection

The traditional ways of candidate selection and recruitment are prone to subjectivity, imprecision, and vagueness. With a view to achieving objective and precise selection and recruitment while keeping up with technological improvement and changes, this paper discusses a fuzzification-based technique for candidate rating and selection. The technique comprises a fuzzy logic component that is an extension of Boolean logic and used for establishing accurate selection process and precise solutions to multi-variable problems. There is a knowledge base component which forms the database of multi-level information and rule base which composes a set of if-then statements for decision making. Its inference engine applies a pre-defined procedure on input from the rule base and fuzzy logic interfaces for final recommendations. The proposed methodology performs predefined procedures that are based on some input sets which store multi-level information derived from several pre-specified scores. Results from the implementation of the proposed technique establish its practical function.