2020/03/22 by Hadi Mansourifar, Weidong Shi, Mansourifar, Hadi +1 · 1 citation
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Industrial Vision Systems and Defect Detection #Infrastructure Maintenance and Monitoring #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2003.09788
arxiv created 2020/03/22 · openalex publication_date 2020/03/22 · arxiv updated 2020/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Synthetic Minority Over-sampling Technique (SMOTE) is the most popular over-sampling method. However, its random nature makes the synthesized data and even imbalanced classification results unstable. It means that in case of running SMOTE n different times, n different synthesized in-stances are obtained with n different classification results. To address this problem, we adapt the SMOTE idea in deep learning architecture. In this method, a deep neural network regression model is used to train the inputs and outputs of traditional SMOTE. Inputs of the proposed deep regression model are two randomly chosen data points which are concatenated to form a double size vector. The outputs of this model are corresponding randomly interpolated data points between two randomly chosen vectors with original dimension. The experimental results show that, Deep SMOTE can outperform traditional SMOTE in terms of precision, F1 score and Area Under Curve (AUC) in majority of test cases.