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- Title
Hybrid GA-DeepAutoencoder-KNN Model for Employee Turnover Prediction.
- Authors
Chin Siang Lim; Malik, Esraa Faisal; Khaw, Khai Wah; Alnoor, Alhamzah; XinYing Chew; Zhi Lin Chong; Al Akasheh, Mariam
- Abstract
Organizations strive to retain their top talent and maintain workforce stability by predicting employee turnover and implementing preventive measures. Employee turnover prediction is a critical task, and accurate prediction models can help organizations take proactive measures to retain employees and reduce turnover rates. Therefore, in this study, we propose a hybrid genetic algorithm-autoencoder-k-nearest neighbor (GA-DeepAutoencoder-KNN) model to predict employee turnover. The proposed model combines a genetic algorithm, an autoencoder, and the KNN model to enhance prediction accuracy. The proposed model was evaluated and compared experimentally with the conventional DeepAutoencoder-KNN and k-nearest neighbor models. The results demonstrate that the GA-DeepAutoencoder-KNN model achieved a significantly higher accuracy score (90.95%) compared to the conventional models (86.48% and 88.37% accuracy, respectively). Our findings are expected to assist human resource teams identify at-risk employees and implement targeted retention strategies to improve the retention rate of valuable employees. The proposed model can be applied to various industries and organizations, making it a valuable tool for human resource professionals to improve workforce stability and productivity.
- Subjects
LABOR turnover; EMPLOYEE reviews; PREDICTION models; GENETIC models; FORECASTING; GENETIC algorithms; K-nearest neighbor classification; DIMENSIONS
- Publication
Statistics, Optimization & Information Computing, 2024, Vol 12, Issue 1, p75
- ISSN
2311-004X
- Publication type
Article
- DOI
10.19139/soic-2310-5070-1799