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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月11日

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  • Tianyi Zhang,
  • Jing Yang,
  • Ru Liu,
  • Qiyuan Feng

摘要

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.