PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm
Abstract Scientific researchers constitute the core strength of innovation within an organization, and their turnover can significantly affect the enterprise. This includes the risk of trade secret disclosure, setbacks in research and development, and stalled business progress. To address these issu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06970-x |
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| _version_ | 1850277628598026240 |
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| author | Tianyi Zhang Jing Yang Ru Liu Qiyuan Feng |
| author_facet | Tianyi Zhang Jing Yang Ru Liu Qiyuan Feng |
| author_sort | Tianyi Zhang |
| collection | DOAJ |
| description | Abstract Scientific researchers constitute the core strength of innovation within an organization, and their turnover can significantly affect the enterprise. This includes the risk of trade secret disclosure, setbacks in research and development, and stalled business progress. To address these issues, this paper proposes a novel prediction method named PAD-SA (Prediction of Academic Departure using ADASYN-Stacking Algorithm) by employing the ADASYN (Adaptive Synthetic) sampling algorithm in conjunction with the Stacking algorithm. PAD-SA can predict the probability of scientific researchers’ departure, thereby helping enterprises anticipate the turnover intentions of their research staff members. The dataset for this study comprises feature information collected from 1100 scientific researchers. The paper addresses the dataset imbalance issue by employing the adaptive oversampling algorithm of ADASYN, which effectively mitigates model prediction bias due to uneven sample distribution. In performance comparisons, PAD-SA outperformed the best model in the benchmark group, with its ROC value exceeding the average performance of the comparative models by 3.7%, 11.9%, and 9.3% respectively. |
| format | Article |
| id | doaj-art-d61270d0107d40df9599b7b6cf3b7d6a |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-d61270d0107d40df9599b7b6cf3b7d6a2025-08-20T01:49:48ZengSpringerDiscover Applied Sciences3004-92612025-05-017511210.1007/s42452-025-06970-xPAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithmTianyi Zhang0Jing Yang1Ru Liu2Qiyuan Feng3State Grid Lanzhou Electric Power Supply CompanyState Grid Gansu Electric Power CompanyState Grid Lanzhou Electric Power Supply CompanyState Grid Gansu Electric Power CompanyAbstract Scientific researchers constitute the core strength of innovation within an organization, and their turnover can significantly affect the enterprise. This includes the risk of trade secret disclosure, setbacks in research and development, and stalled business progress. To address these issues, this paper proposes a novel prediction method named PAD-SA (Prediction of Academic Departure using ADASYN-Stacking Algorithm) by employing the ADASYN (Adaptive Synthetic) sampling algorithm in conjunction with the Stacking algorithm. PAD-SA can predict the probability of scientific researchers’ departure, thereby helping enterprises anticipate the turnover intentions of their research staff members. The dataset for this study comprises feature information collected from 1100 scientific researchers. The paper addresses the dataset imbalance issue by employing the adaptive oversampling algorithm of ADASYN, which effectively mitigates model prediction bias due to uneven sample distribution. In performance comparisons, PAD-SA outperformed the best model in the benchmark group, with its ROC value exceeding the average performance of the comparative models by 3.7%, 11.9%, and 9.3% respectively.https://doi.org/10.1007/s42452-025-06970-xTurnover intentionMachine learningIntegrated learningPAD-SA |
| spellingShingle | Tianyi Zhang Jing Yang Ru Liu Qiyuan Feng PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm Discover Applied Sciences Turnover intention Machine learning Integrated learning PAD-SA |
| title | PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm |
| title_full | PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm |
| title_fullStr | PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm |
| title_full_unstemmed | PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm |
| title_short | PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm |
| title_sort | pad sa a method for predicting the turnover of scientific researchers based on adasyn stacking algorithm |
| topic | Turnover intention Machine learning Integrated learning PAD-SA |
| url | https://doi.org/10.1007/s42452-025-06970-x |
| work_keys_str_mv | AT tianyizhang padsaamethodforpredictingtheturnoverofscientificresearchersbasedonadasynstackingalgorithm AT jingyang padsaamethodforpredictingtheturnoverofscientificresearchersbasedonadasynstackingalgorithm AT ruliu padsaamethodforpredictingtheturnoverofscientificresearchersbasedonadasynstackingalgorithm AT qiyuanfeng padsaamethodforpredictingtheturnoverofscientificresearchersbasedonadasynstackingalgorithm |