Binary Cross Entropy

Binary cross-entropy (BCE) is a loss function commonly used in machine learning to train binary classifiers, aiming to minimize the difference between predicted probabilities and true labels. Recent research focuses on adapting BCE for various challenges, including imbalanced datasets (e.g., in click-through rate prediction), noisy labels (as in partial multi-label learning), and concept drift (seen in applications like malware detection). These adaptations often involve modifying BCE's formulation, such as incorporating asymmetric weighting or dynamic penalties, or combining it with other techniques like label smoothing or feature reduction to improve model performance and calibration. The resulting improvements in model accuracy and robustness have significant implications across diverse fields, from recommender systems and malware detection to audio-visual synchronization and communication systems.