A Comparative Analysis of Classification Methods to Multi-label Tasks in Different Application Domains

Abstract

In traditional classification problems (single-label), patterns are usually associated with a single label from a set of two or more classes. When an example can simultaneously belong to more than one class (label), this classification problem is known as multi-label classification problem. Multi-label classification methods have been increasingly used in modern applications, such as music categorization, functional genomics and semantic annotation of images. In addition, the multi-label classification methods can be broadly classified in two groups, which are: problem transformation and algorithm adaptation methods. This paper presents a comparative analysis of some existing multi-label classification methods (from both groups of methods) applied to different problem domains. The main aim of this analysis is to evaluate the performance of such methods in different tasks and using different evaluation metrics.