An Extensive Evaluation of Decision Tree-Based Hierarchical Multi-Label Classification Methods and Performance Measures pdfsubject=Hierarchical Multi-Label Classification

Hierarchical Multi-Label Classification is a complex classification problem where an instance can be assigned to more than one class simultaneously, and these classes are hierarchically organized with superclasses and subclasses, i.e., an instance can be classified as belonging to more than one path in the hierarchical structure. This article experimentally analyses the behaviour of different decision tree-based hierarchical multi-label classification methods based on the local and global classification approaches. The approaches are compared using distinct hierarchy-based and distance-based evaluation measures, when they are applied to a variation of real multilabel and hierarchical datasets’ characteristics. Also, the different evaluation measures investigated are compared according to their degrees of consistency, discriminancy and indifferency. As a result of the experimental analysis, we recommend the use of the global classification approach and suggest the use of the Hierarchical Precision and Hierarchical Recall evaluation measures.