Abstract:
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 research proposes a fuzzification-based technique for candidate rating and selection by financial institutions. 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 comprises 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 pre-defined 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 established its practical function as well as its superior performances over some of the existing models for the same purpose
