Sequence Classification

Sequence classification, the task of assigning labels to sequential data, aims to extract meaningful patterns and insights from ordered information like DNA sequences, time series, or text. Current research emphasizes improving model performance, particularly in scenarios with limited data or complex dependencies, through techniques like generative augmentation, ensemble methods (including Hidden Markov Models), and novel neural architectures such as hypergraph attention networks and circular dilated convolutional neural networks. These advancements have significant implications across diverse fields, enabling improved accuracy in applications ranging from medical diagnosis and genomic analysis to music information retrieval and anomaly detection in industrial processes.