Decision Boundary

A decision boundary in machine learning defines the separation between different classes in a feature space, with the primary objective being to create a boundary that accurately classifies unseen data. Current research focuses on improving decision boundary robustness and stability, particularly addressing challenges like class imbalance, noisy labels, and adversarial attacks, often employing techniques within neural networks (including convolutional and transformer architectures) and leveraging methods such as mixup, contrastive learning, and distributionally robust optimization. Understanding and controlling the properties of decision boundaries is crucial for building reliable and robust machine learning models across diverse applications, from network traffic classification to anomaly detection and open-set learning.