Abstract: A network model SNSMRS (SMOTEENN-mixed residual networks-SVM network) based on hybrid sampling, squeeze and excitation (SE) module, improved deep residual network and support vector machines (SVM) is proposed to address the problem of uneven class distribution of unbalanced data sets in traditional structured multiclassification algorithms, which leads to increased classification difficulty. Firstly, the data distribution is improved by synthesizing minority oversampling and editing nearest neighbors technique. Then the features are extracted by combining SE module and a deep residual network, improved with batch normalization and group normalization. Finally, the network model uses support vector machine (SVM) to output the classification results. The SE module enhances the model’s feature differentiation ability and robustness. The improvements to the ResNet, through fusion normalization, mitigate issues such as gradient vanishing and accuracy degradation, and ensure stability and accuracy regardless of batch_size. Additionally, SVM enhances the classification accuracy by effectively utilizing feature vectors in space to classify and extract features. Comparison and ablation experiments are conducted on seven unbalanced public datasets of various sizes and domains. The experimental results show that the proposed model, SNSMRS, not only outperforms other deep learning models, but also increases the values of Macro-F1 and G-mean by approximately 3% and 4%, respectively, compared with the original ResNet. Macro-F1 and G-mean values of SNSMRS exceed 95% on four of the datasets, demonstrating its superior performance.

Key words: unbalanced multi-classification, mixed sampling, squeeze and excitation (SE) module, group normalization, ResNet, support vector machines (SVM)