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PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm

Guardat en:
Publicat a:SN Applied Sciences vol. 7, no. 5 (May 2025), p. 481
Publicat:
Springer Nature B.V.
Matèries:
Accuracy
Bias
Datasets
Sampling techniques
Algorithms
Adaptive sampling
Human resource management
Fraud prevention
Researchers
Research & development > R&D
Sampling
Prediction models
Efficiency
Adaptive algorithms
Machine learning
Performance assessment
Sampling methods
Support vector machines
Classification
Employee turnover
Turnover rate
Economic
Accés en línia:Citation/Abstract
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Resum: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>
ISSN:2523-3963
2523-3971
DOI:10.1007/s42452-025-06970-x
Font:Science Database