RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers

Keyur Patel, Karan Sheth, Dev Mehta, Sudeep Tanwar (), Bogdan Cristian Florea, Dragos Daniel Taralunga (), Ahmed Altameem, Torki Altameem and Ravi Sharma
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Keyur Patel: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Karan Sheth: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Dev Mehta: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Bogdan Cristian Florea: Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Dragos Daniel Taralunga: Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Ahmed Altameem: Computer Science Department, Community College, King Saud University, Riyadh 11451, Saudi Arabia
Torki Altameem: Computer Science Department, Community College, King Saud University, Riyadh 11451, Saudi Arabia
Ravi Sharma: Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248007, India

Mathematics, 2022, vol. 10, issue 19, 1-21

Abstract: An organization’s success depends on its employees, and an employee’s performance decides whether the organization is successful. Employee performance enhances the productivity and output of organizations, i.e., the performance of an employee paves the way for the organization’s success. Hence, analyzing employee performance and giving performance ratings to employees is essential for companies nowadays. It is evident that different people have different skill sets and behavior, so data should be gathered from all parts of an employee’s life. This paper aims to provide the performance rating of an employee based on various factors. First, we compare various AI-based algorithms, such as random forest, artificial neural network, decision tree, and XGBoost. Then, we propose an ensemble approach, RanKer , combining all the above approaches. The empirical results illustrate that the efficacy of the proposed model compared to traditional models such as random forest, artificial neural network, decision tree, and XGBoost is high in terms of precision, recall, F1-score, and accuracy.

Keywords: employee performance; machine learning; ensemble learning; low performer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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