<i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers
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, anal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/19/3714 |
| _version_ | 1797478033228038144 |
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| author | Keyur Patel Karan Sheth Dev Mehta Sudeep Tanwar Bogdan Cristian Florea Dragos Daniel Taralunga Ahmed Altameem Torki Altameem Ravi Sharma |
| author_facet | Keyur Patel Karan Sheth Dev Mehta Sudeep Tanwar Bogdan Cristian Florea Dragos Daniel Taralunga Ahmed Altameem Torki Altameem Ravi Sharma |
| author_sort | Keyur Patel |
| collection | DOAJ |
| description | 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, <i>RanKer</i>, 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. |
| first_indexed | 2024-03-09T21:26:16Z |
| format | Article |
| id | doaj.art-14d11884e9444c05897d494775a6660e |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T21:26:16Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-14d11884e9444c05897d494775a6660e2023-11-23T21:06:17ZengMDPI AGMathematics2227-73902022-10-011019371410.3390/math10193714<i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low PerformersKeyur Patel0Karan Sheth1Dev Mehta2Sudeep Tanwar3Bogdan Cristian Florea4Dragos Daniel Taralunga5Ahmed Altameem6Torki Altameem7Ravi Sharma8Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, RomaniaDepartment of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, RomaniaComputer Science Department, Community College, King Saud University, Riyadh 11451, Saudi ArabiaComputer Science Department, Community College, King Saud University, Riyadh 11451, Saudi ArabiaCentre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248007, IndiaAn 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, <i>RanKer</i>, 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.https://www.mdpi.com/2227-7390/10/19/3714employee performancemachine learningensemble learninglow performer |
| spellingShingle | Keyur Patel Karan Sheth Dev Mehta Sudeep Tanwar Bogdan Cristian Florea Dragos Daniel Taralunga Ahmed Altameem Torki Altameem Ravi Sharma <i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers Mathematics employee performance machine learning ensemble learning low performer |
| title | <i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers |
| title_full | <i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers |
| title_fullStr | <i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers |
| title_full_unstemmed | <i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers |
| title_short | <i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers |
| title_sort | i ranker i an ai based employee performance classification scheme to rank and identify low performers |
| topic | employee performance machine learning ensemble learning low performer |
| url | https://www.mdpi.com/2227-7390/10/19/3714 |
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