Single Label

Single-label classification focuses on assigning a single label to each data point, a task prevalent across diverse fields from image recognition to natural language processing. Current research explores the limitations of single-label approaches, particularly in scenarios with inherent ambiguity or multiple relevant labels, leading to investigations into multi-label alternatives and hybrid methods. This research leverages various model architectures, including deep neural networks (like transformers and CNNs), reinforcement learning, and evolutionary algorithms, to improve accuracy and efficiency. The development of robust single-label and multi-label classification methods has significant implications for numerous applications, including improved accuracy in various prediction tasks and more efficient use of data.