Forest Classifier

Forest classifiers, a type of ensemble learning method, aim to improve classification accuracy and robustness by combining multiple decision trees. Current research emphasizes enhancing these models through techniques like incorporating fuzzy logic, handling high-dimensional data with low sample sizes, and adapting them for time series and complex data types, including the use of pre-trained transformer models. These advancements are impacting diverse fields, from solar flare prediction and medical diagnosis to malware detection, by providing more accurate and interpretable classification models for complex datasets.