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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Keyur Patel & Karan Sheth & Dev Mehta & Sudeep Tanwar & Bogdan Cristian Florea & Dragos Daniel Taralunga & Ahmed Altameem & Torki Altameem & Ravi Sharma, 2022. "RanKer : An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers," Mathematics, MDPI, vol. 10(19), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3714-:d:938291
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    References listed on IDEAS

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    1. Vasu Kalariya & Pushpendra Parmar & Patel Jay & Sudeep Tanwar & Maria Simona Raboaca & Fayez Alqahtani & Amr Tolba & Bogdan-Constantin Neagu, 2022. "Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market," Mathematics, MDPI, vol. 10(9), pages 1-15, April.
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    Cited by:

    1. Pletcher, Scott Nicholas, 2023. "Practical and Ethical Perspectives on AI-Based Employee Performance Evaluation," OSF Preprints 29yej, Center for Open Science.

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