Abstract:To tackle the classification problem of high-dimensional group variables, this study proposes an MCP-based AdaBoost ensemble-pruning logistic regression model (AdaMCPLR). The MCP function is applied to feature selection and ensemble pruning simultaneously, which not only simplifies the model, but also effectively improves the prediction accuracy. For the efficiency enhancement, this paper improves the PICASSO algorithm to make it applicable to group variable selection. Simulation experiments show that the AdaMCPLR method is superior to other prediction methods in variable selection and classification prediction. Finally, the AdaMCPLR method proposed in this study is applied to the financial distress prediction of listed companies in China.