Abstract:The imbalance of classes in real-world datasets poses a major challenge in machine learning and classification, and traditional synthetic data generation methods often fail to address this problem effectively. A major limitation of these methods is that they tend to separate the process of generating synthetic samples from the training process, resulting in synthetic data that lack the necessary informative characteristics for proper model training. We present a new synthetic data generation method that addres… Show more
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