No abstract

Data imbalance is one of the problems that we face when applying machine learning to real-world problems, especially in image classification. With all the improvements in machine learning, especially deep learning, research in this area is drawing more attention from academics and even industry. To address this imbalanced data problem, we adopt a hybrid (algorithm and data) approach that consists of data manipulation and weighted loss function in this paper. We propose Ripple-SMOTE as a novel oversampling method to generate synthetic data for preprocessing. A deep neural network and the weighted loss function is applied so it will not treat all classes equally. We also use a pre-trained model and fine tune it to improve the classification accuracy. In this paper, we report the evaluation results using imbalanced data sets based on MNIST, CUReT texture set, and Malware data set, and show that our approach significantly improves the performance in imbalanced data cases and outperforms the conventional approaches, especially in handling minority classes.