Optimal Classifier

Optimal classifier research aims to identify and develop classification methods that achieve the best possible performance, often defined as minimizing error rates or maximizing accuracy. Current research focuses on addressing challenges like imbalanced datasets, noisy labels, adversarial attacks, and fairness concerns, employing techniques such as Support Vector Machines (SVMs), various ensemble methods, and deep neural networks adapted for specific problem contexts. These advancements have significant implications for diverse applications, improving the reliability and robustness of machine learning systems across various fields, from medical diagnosis to fraud detection.