Automating Decision-Making for Hiring Brilliant People While Taking Risk Factors Into Account: A Data Mining Approach

Abstract

In order to pick multi-talented employees from a large number of resumes, the human resource department (HR) is required to apply more accurate talent evaluation programs. However, rather of focusing on risk issues, the majority of talent evaluation tools evaluate talent. This article suggests a technique for selecting qualified competent staff resumes without taking risks into account using the technology for data mining called mining by association rules (ARM). The system's automatic intelligence agents (AIAS), which was created making decisions using a knowledge-based system based using logic principles and data gathered employing the ARM methodology, information from subject matter experts and prior learning experiences, directs the activities of the HR Department. The relevant experimental findings from AIAS allow HR departments to quickly decide who to hire for talent employees without wasting time for both candidates and employers during interviews. The useful experimental findings from AIAS allow HR departments to make quick selections for accurately hiring talented employees without squandering both employer and candidate time during interviews.