Employee Attrition Rate Prediction Using Improved Sparrow Search Algorithm-based Deep Belief Network
Keywords:
Adaptive Synthetic Sampling technique, Chaos mapping, Deep Learning, Employee Attrition Rate, Improved Sparrow Search AlgorithmAbstract
Employee attrition is a natural procedure by which employees leave the workspace because of uncertain factors like retirement, resignation for personal reasons, and are not replaced immediately. Decision-making plays a significant role in management and it is the most essential element in the planning process. However, Employee attrition is regarded as a well-known issue that requires the right decisions from administration to provide highly qualified employees. Therefore, the Improved Sparrow Search Algorithm-based Deep Belief Network (ISSA-DBN) is proposed to predict whether the employee is leaving or staying at the company using Deep Learning (DL).SSA is improved by using chaos mapping to generate increased population diversity, Adaptive inertia weight for updating the finder position, and an Adaptive t-distribution approach to solving the premature convergence. Initially, the IBM HR dataset is utilized to evaluate the proposed approach, and min-max normalization is established to improve the model performances. The Adaptive Synthetic Sampling technique (ADASYN) is used to balance the imbalanced data. Then, the ISSA is performed to select the appropriate features. Finally, DBN is employed for employee attrition rate prediction. The proposed ISSA-DBN achieves a high accuracy of 0.99 compared to existing techniques like Deep Neural Networks (DNN), max out Logistic Regression (LR), and deep data-driven technique using a Voting Classifier (VC) respectively.
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Dataset Link: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset
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