Incorporando correlações entre exemplos para classificação multirrótulo via espaço de classes

Abstract

Multi-label classification is a machine learning task where instances can be classified into two or more labels simultaneously. In this task, there exist correlations between the instances belonging to same or similar sets of labels. This work proposes the incorporation of instance correlations by modifying the multi-label datasets. The label-space was used to create new features, which represent these correlations. The original and modified datasets were used with different multi-label classification methods. Experiments have shown that the method obtained better classification results in comparison with its original datasets counterparts for most of the algorithms. All methods were evaluated with measures specifically designed for multi-label problems.