<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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Autor*in:	Keyur Patel [verfasserIn] Karan Sheth [verfasserIn] Dev Mehta [verfasserIn] Sudeep Tanwar [verfasserIn] Bogdan Cristian Florea [verfasserIn] Dragos Daniel Taralunga [verfasserIn] Ahmed Altameem [verfasserIn] Torki Altameem [verfasserIn] Ravi Sharma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	employee performance machine learning ensemble learning low performer

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 10(2022), 19, p 3714
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:19, p 3714

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math10193714

Katalog-ID:	DOAJ028268865

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topic_title	QA1-939 <i<RanKer</i<: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers employee performance machine learning ensemble learning low performer
topic	misc QA1-939 misc employee performance misc machine learning misc ensemble learning misc low performer misc Mathematics
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title	<i<RanKer</i<: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers
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title_auth	<i<RanKer</i<: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers
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, <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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	<i<RanKer</i<: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers
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