Imbalanced data learning using SMOTE and deep learning architecture with optimized features

Learning from a class-imbalanced data poses a challenging task in machine learning. Because the skewness in imbalanced data is inherent; therefore, the number of instances is not equal. The class with few samples is identified as the minority class, and a class with many instances is referred to as the majority class. Due to this, biased learning is caused by the machine learning models. When the dataset had more features, the classifier’s performance also suffered. A common method for choosing the crucial features from a dataset and enhancing the performance of a machine learning classifier is feature selection. The feature selection cannot be handled by oversampling approaches alone. Therefore, the purpose of the suggested strategy is to enhance deep learning classifier performance by feature selection-based oversampling. The three modules that make up the suggested methodology are deep 1D-convolutional neural network (DCNN) for classification, SMOTE for oversampling, and genetic algorithm for feature selection. High-dimensional datasets with class imbalance issues are considered by the proposed method. There are both small and large datasets used. In addition, the datasets with low and large imbalance ratios are considered. When compared to benchmark methods, the suggested solution performs well.