Cost Sensitive

Cost-sensitive learning addresses the challenge of unequal misclassification costs in machine learning, aiming to optimize models for real-world scenarios where different types of errors have varying consequences. Current research focuses on adapting existing algorithms like Support Vector Machines and boosting methods, as well as developing novel approaches such as instance-complexity-based weighting and adversarial data augmentation, particularly within deep learning frameworks and graph neural networks. This field is crucial for improving the reliability and practical applicability of machine learning models in high-stakes domains like healthcare, finance, and fraud detection, where minimizing specific error types is paramount.