Rule Generation for Recruitment Decision Analysis-A Granular Approach

Abstract

The main aim of this paper is to analyze the various recruitment decision making policies using granular computing based on rough set perspective and fuzzy distance approach for recruiting a candidate in any organization. An information table has been presented in this article which consists of various factors like appearance, qualification, experience and communication skills to evaluate a candidate for recruitment. Depending upon the granularity of knowledge obtained from the information table, the concept of rough set has been applied to generate fuzzy decision rules which in turns form the eligibility criteria for the candidates appearing in the interview for recruitment. The experts in the interview committee have stated their opinion about the candidates linguistically. A relationship has been established between the eligibility criteria required for the job and the expert opinions about the candidates appeared in the interview using a fuzzy subset representation. The index of fuzziness of various experts’ opinion are measured and compared. A candidate with higher grade of merit has been selected using fuzzy distance approach.