Ensemble learning method based on CNN for class imbalanced data

Classifying imbalanced data presents a significant challenge, and many studies have proposed methodologies to address this issue. Among them, Convolutional Neural Networks have demonstrated superior performance for imbalanced image classification. This paper initially employs various data pre-processing methods such as over-sampling, under-sampling, and SMOTE to enhance the original dataset. Subsequently, an Ensemble CNN learning model is used to train and predict the data. In order to comprehensively evaluate models trained on imbalanced data, we used metrics such as Accuracy, Recall, Precision, F1-score, and G-mean. On the CIFAR-10 and Fashion-MNIST datasets, different samples from each category were extracted as imbalanced data for experimental research. Compared to the AdaBoost-DenseNet model, our proposed methodology increases the test accuracy on the CIFAR-10 dataset by 9%. Similarly, the F1-score and G-mean improved by 0.096 and 0.069, respectively. Compared to traditional methodologies, our proposed method significantly improves accuracy, recall, precision, and other performance indicators.