Class Classifier

Class classifiers are machine learning models designed to assign data points to one of several predefined categories. Current research emphasizes improving classifier accuracy and efficiency across diverse applications, focusing on techniques like support vector machines (SVMs), ensemble methods, and neural networks (including transformers), often tailored to handle imbalanced datasets or noisy labels. These advancements are crucial for various fields, from medical diagnosis (e.g., melanoma detection, Alzheimer's disease diagnosis) and industrial fault prediction to cybersecurity and assistive technologies (e.g., gait phase classification for prosthetics). The development of robust and efficient class classifiers continues to be a significant area of investigation, driving improvements in both theoretical understanding and practical applications.