Study on the application of data visualization technology in human resource management and employee turnover prediction

Abstract

This paper presents a method to process the dataset of departing employees. The clustering categories of separated employees are accurately determined by fuzzy c-mean clustering and improved clustering FCM algorithm, and new samples of separated employees are generated using SMOTE algorithm to reduce noisy data. The kernel function trick of SVM is used to achieve clustering oversampling, improving classification accuracy. For imbalanced data, this paper uses a new integrated learning algorithm for constructing evaluations, PIBoost, combined with a full-sample cost-aware weight algorithm to improve the generalization ability of the SVM classifier, which has better classification results for various data sets. In the model performance comparison, K-AFCM-SMOTE-SVM has the highest accuracy with a value of 0.89. In the ten-fold cross-validation accuracy comparison, the K-AFCM-SMOTE-SVM model has a better overall performance index than the other two, with an average cross-validation accuracy of 0.932.

Keywords

• employee separation prediction
• mean clustering
• fcm algorithm
• smote algorithm
• svm
• 91b44