PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm
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| Pubblicato in: | SN Applied Sciences vol. 7, no. 5 (May 2025), p. 481 |
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| Pubblicazione: |
Springer Nature B.V.
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| Accesso online: | Citation/Abstract Full Text Full Text - PDF |
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| 045 | 2 | |b d20250501 |b d20250531 | |
| 245 | 1 | |a PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm | |
| 260 | |b Springer Nature B.V. |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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.Article Highlights<list list-type="bullet"><list-item></list-item>Through visualization techniques, the relationship between dataset features and employee turnover rates is revealed, laying the foundation for data preprocessing and model construction.<list-item>The ADASYN sampling technique is employed to address the imbalance in the original dataset, effectively reducing the prediction bias of the model.</list-item><list-item>By integrating the Stacking algorithm, an efficient prediction model for the turnover of researchers is successfully constructed, yielding significant results.</list-item> | |
| 653 | |a Accuracy | ||
| 653 | |a Bias | ||
| 653 | |a Datasets | ||
| 653 | |a Sampling techniques | ||
| 653 | |a Algorithms | ||
| 653 | |a Adaptive sampling | ||
| 653 | |a Human resource management | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Researchers | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Sampling | ||
| 653 | |a Prediction models | ||
| 653 | |a Efficiency | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Machine learning | ||
| 653 | |a Performance assessment | ||
| 653 | |a Sampling methods | ||
| 653 | |a Support vector machines | ||
| 653 | |a Classification | ||
| 653 | |a Employee turnover | ||
| 653 | |a Turnover rate | ||
| 653 | |a Economic | ||
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 5 (May 2025), p. 481 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203967204/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3203967204/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203967204/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |