Employee Attrition Classification using Improved Deep Belief Networks

Abstract

Companies are often worried about employee turnover since it affects productivity, morale, and expenses. It is common for traditional attrition prediction approaches to be inaccurate and to not make full use of the data that is available. Using Improved Deep Belief Networks (IDBN), we provide a state-of-the-art method for employee turnover classification in this research. The first of the two steps in the approach is feature selection, which is used to choose the best variables to use for prediction. Then, using this improved dataset, the IDBN model is trained to correctly identify stable and attrition-prone personnel. Integrating IDBN improves the model's capacity to detect intricate patterns and connections in the data, which is the main contribution of this study. To top it all off, the feature selection process guarantees that the model uses high-quality inputs, which boosts classification accuracy even further. The suggested strategy outperforms conventional approaches and achieves better accuracy in forecasting employee turnover, as shown experimentally using real-world datasets. If your company is looking to optimize worker stability and proactively control attrition, this study gives significant information.