Bayes Classifier

Bayes classifiers are probabilistic models aiming to assign data points to classes based on maximizing posterior probabilities, offering both predictive accuracy and inherent uncertainty quantification. Current research focuses on improving robustness in various contexts, including handling imbalanced datasets, noisy labels, and data scarcity, often employing techniques like variational autoencoders and distributionally robust optimization to enhance performance. These advancements are significant for applications ranging from geotechnical engineering (e.g., sediment classification) to network security (e.g., intrusion detection), where reliable classification with uncertainty estimates is crucial for informed decision-making. Furthermore, ongoing work explores the interplay between fairness, accuracy, and data restrictions in Bayes classifiers, highlighting the need for responsible algorithm design.