Neural network fusion with fine-grained adaptation learning for turnover prediction

Turnover prediction has an important impact on alleviating the brain drain, which can help organizations reduce costs and enhance competitiveness. Existing studies on turnover are mainly based on analyzing the turnover correlation, using different models to predict various employee turnover scenarios, and only predicting turnover category, while the class imbalance and turnover possibility have been ignored. To this end, in this paper, we propose a novel fine-grained adaptation-based turnover prediction neural network (FATPNN) model. Specifically, we first employ a GRU to learn profile-aware features representations of the personnel samples. Then, to evaluate the contribution of various turnover factors, we further exploit an attention mechanism to model the profile information. Finally, we creatively design a weighted-based probability loss function suitable for our turnover prediction tasks. Experimental results show the effectiveness and universality of the FATPNN model in terms of turnover prediction.

Automated summary

In this paper, a novel employee turnover prediction model called FATPNN is proposed. The model learns feature representations of personnel samples using GRU and employs an attention mechanism to model profile information, resulting in an effective prediction of employee turnover.