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

This paper examines the usefulness of logit regression in forecasting consumer bankruptcy households using an imbalanced dataset. The research on prediction is paramount importance as it aims to build statistical models that can identify consumers a difficult financial situation may lead bankruptcy. In face current global pandemic crisis, future household finances uncertain. change macroeconomi...

The classification of imbalanced datasets, especially in medicine, is a major problem data mining. Such evident analyzing normal and abnormal subjects about knee from collected during walking. In this work, surface electromyography (sEMG) were walking the lower limb 22 individuals (11 with 11 without abnormality). Subjects abnormality take longer to complete task than healthy subjects. Therefor...

Class imbalance is one of the most basic and important problems web data. The key to overcoming class increase effective instances minority, that is, data augmentation. Generative Adversarial Networks (GANs), which have recently been successfully applied in field image generation, can be used for augmentation because they learn distribution given ample training generate more However, learning d...