Ternary Classification

Ternary classification, assigning data points to one of three categories, is a crucial task across diverse fields, driven by the need for more nuanced analysis than binary classification allows. Current research focuses on improving the accuracy and efficiency of ternary classifiers, employing various machine learning models such as Support Vector Machines, Recurrent Neural Networks (including LSTMs and GRUs), Transformers (including Vision Transformers and BERT), and Hidden Markov Models, often tailored to specific applications. These advancements have significant implications for applications ranging from sentiment analysis of social media data and medical image segmentation to improved diagnostics in healthcare and efficient signal processing.