Imbalanced Data

Imbalanced data, where one class significantly outnumbers others, poses a major challenge for machine learning models, hindering their ability to accurately predict minority classes. Current research focuses on mitigating this imbalance through techniques like data augmentation (e.g., using generative adversarial networks or variational autoencoders), cost-sensitive learning, and novel loss functions designed to be robust to skewed distributions; various model architectures, including Recurrent Neural Networks, Transformers, and ensemble methods, are being adapted and evaluated for their effectiveness in these scenarios. Addressing imbalanced data is crucial for improving the reliability and fairness of machine learning models across diverse applications, from medical image analysis and fraud detection to cybersecurity and industrial process monitoring.