The classification method based on evolutionary algorithm for high-dimensional imbalanced missing data

High dimensional imbalanced missing data classification is a challenging and complex problem that traditional algorithms struggle to solve effectively. To address this issue, a novel method is proposed, named the hybrid classification approach based on particle swarm optimization (HCPSO). HCPSO integrates ideas of feature selection, resampling and imputation, and breaks particles down into three parts. These parts represent the feature values, probabilities of resampling, and probabilities of imputation approaches, respectively. Moreover, HCPSO employs particle swarm optimization to optimize these parameters simultaneously to take advantage of these methods. Six types of algorithms, eleven datasets, and four performance indicators are used to evaluate our method. The results demonstrate a significant improvement in HCPSO's performance, with an average improvement of 13.02%, 18.95%, 20.25%, and 28.63% for accuracy, F1, AUC, and Gmean, respectively, compared to all other methods. Furthermore, the experiments also demonstrate the robustness of HCPSO.