Evaluating classification methods applied to multi-label tasks in different domains

Abstract

In traditional classification problems (single-label), patterns are associated with a single label from the set of disjoint labels (classes). When an example can simultaneously belong to more than one label, this classification problem is known as multi-label classification problem. Multi-label classification methods have been increasingly used in modern application, such as music categorization, functional genomics and semantic annotation of images. This paper presents a comparative analysis of some existing multi-label classification methods applied to different domains. The main aim of this analysis is to evaluate the performance of such methods in different tasks and using different evaluation metrics.