Label Dependency

Label dependency, the interconnectedness of labels in multi-label classification tasks, is a crucial area of research aiming to improve prediction accuracy and model interpretability. Current research focuses on mitigating biases stemming from spurious correlations between labels, developing methods to leverage label relationships even with limited annotation, and employing graph neural networks and attention mechanisms to effectively model these dependencies. Understanding and appropriately utilizing label dependency is vital for enhancing the performance of multi-label classification models across diverse applications, including text classification, image recognition, and emotion recognition, leading to more accurate and explainable predictions.