Towards a High-Level Multi-label Classification from Complex Networks

Multi-label learning aims to solve problems in which data items can have multiple class labels assigned simultaneously, e.g., text categorization, image annotation, medical diagnosis, etc. However, as most of multi-label techniques are derived from the single-label ones, existing techniques perform the multi-label classification only based on the physical features of the data (e.g., distance, similarity or distribution), ignoring the semantic meaning of the data, such as the formation pattern. Inspired by recent advances in the use of complex networks for single-label learning, this exploratory work aims to investigate a multi-label solution able to combine existing multi-label classifiers with a high-level classifier based on complex networks measures, aiming to present a new concept of multi-label classification that, besides the physical attributes, also analyzes the topological structure of the data. Experimental results considering both artificial and real-world data sets emphasize respectively the salient features of our technique in comparison to the traditional ones and its potential to improve the predictive performance of those techniques, especially in data sets characterized by higher cardinality and density of labels, which often denote more difficult scenarios to multi-label learning.