Progressive Multi-Label Classification (PMLC) is a machine learning technique designed to address complex classification problems where each instance can belong to multiple categories simultaneously. Unlike traditional multi-label classification, PMLC takes into account the hierarchical nature of labels and the order in which labels are predicted, allowing for a more efficient and accurate classification process. In PMLC, labels are organized in a hierarchy or a taxonomy, reflecting the relationships between them. This hierarchy is often represented as a directed acyclic graph (DAG), where parent labels represent broader categories, and child labels represent more specific subcategories. The key idea behind PMLC is to make the classification process progressive, meaning that labels are predicted in a structured order, starting from the most general and moving towards the most specific labels. This approach is advantageous because it reduces the label space's dimensionality and makes predictions more interpretable. The PMLC process typically involves two main stages: training and prediction. During the training stage, a model is trained using the hierarchical label structure. The model learns to predict labels in a progressive manner by starting with the root of the hierarchy and moving down towards the leaf nodes. This hierarchical training process is often done using a top-down or bottom-up approach, where either the most general or the most specific labels are predicted first. The choice of approach depends on the problem and the structure of the label hierarchy. One common algorithm used in PMLC is the hierarchical classifier chain (HCC). In HCC, each label is associated with a separate binary classifier. Labels are ordered based on the hierarchical structure, and each classifier is trained to predict its corresponding label, taking into account the predictions of its ancestor labels. This way, the classifiers use the information from higher-level labels to assist in predicting lower-level labels. This progressive prediction mechanism aligns with the hierarchical structure and ensures that the predictions respect the relationships between labels.