Sequential Classifier

Sequential classification addresses the problem of classifying data points arriving sequentially, aiming to optimize both classification accuracy and processing efficiency. Current research focuses on developing algorithms that efficiently handle imbalanced datasets, incorporate early stopping mechanisms for reduced computational cost, and address multi-class and multi-label scenarios, often employing techniques like graph neural networks, hierarchical cascades of binary classifiers, and probabilistic models. These advancements have significant implications for various applications, including intrusion detection, misinformation analysis, and real-time decision-making systems where rapid and accurate classification is crucial.