A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning
Abstract Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization,...
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| Autor*in: |
Jain, Nishant [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent information systems - Springer US, 1992, 56(2020), 2 vom: 29. Sept., Seite 279-302 |
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| Übergeordnetes Werk: |
volume:56 ; year:2020 ; number:2 ; day:29 ; month:09 ; pages:279-302 |
| Links: |
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| DOI / URN: |
10.1007/s10844-020-00614-9 |
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OLC2124405217 |
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| 520 | |a Abstract Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms. | ||
| 650 | 4 | |a Employee churn | |
| 650 | 4 | |a Employee importance model | |
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| 700 | 1 | |a Tomar, Abhinav |4 aut | |
| 700 | 1 | |a Jana, Prasanta K. |4 aut | |
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10.1007/s10844-020-00614-9 doi (DE-627)OLC2124405217 (DE-He213)s10844-020-00614-9-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn 54.00 bkl Jain, Nishant verfasserin (orcid)0000-0003-1010-3735 aut A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms. Employee churn Employee importance model Retention policy CatBoost algorithm MADM method TOPSIS Tomar, Abhinav aut Jana, Prasanta K. aut Enthalten in Journal of intelligent information systems Springer US, 1992 56(2020), 2 vom: 29. Sept., Seite 279-302 (DE-627)171028333 (DE-600)1141899-0 (DE-576)03304032X 0925-9902 nnns volume:56 year:2020 number:2 day:29 month:09 pages:279-302 https://doi.org/10.1007/s10844-020-00614-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2244 54.00 VZ AR 56 2020 2 29 09 279-302 |
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10.1007/s10844-020-00614-9 doi (DE-627)OLC2124405217 (DE-He213)s10844-020-00614-9-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn 54.00 bkl Jain, Nishant verfasserin (orcid)0000-0003-1010-3735 aut A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms. Employee churn Employee importance model Retention policy CatBoost algorithm MADM method TOPSIS Tomar, Abhinav aut Jana, Prasanta K. aut Enthalten in Journal of intelligent information systems Springer US, 1992 56(2020), 2 vom: 29. Sept., Seite 279-302 (DE-627)171028333 (DE-600)1141899-0 (DE-576)03304032X 0925-9902 nnns volume:56 year:2020 number:2 day:29 month:09 pages:279-302 https://doi.org/10.1007/s10844-020-00614-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2244 54.00 VZ AR 56 2020 2 29 09 279-302 |
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A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning |
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Jain, Nishant |
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Jain, Nishant Tomar, Abhinav Jana, Prasanta K. |
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a novel scheme for employee churn problem using multi-attribute decision making approach and machine learning |
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A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning |
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Abstract Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstractGer |
Abstract Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning |
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