Aggregating Independent and Dependent Models to Learn Multi-label Classifiers
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The aim of multi-label classification is to automatically obtain models able to tag objects with the labels that better describe them. Despite it could seem like any other classification task, it is widely known that exploiting the presence of certain correlations between labels helps to improve the classification performance. In other words, object descriptions are usually not enough to induce good models, also label information must be taken into account. This paper presents an aggregated approach that combines two groups of classifiers, one assuming independence between labels, and the other considering fully conditional dependence among them. The framework proposed here can be applied not only for multi-label classification, but also in multi-label ranking tasks. Experiments carried out over several datasets endorse the superiority of our approach with regard to other methods in terms of some evaluation measures, keeping competitiveness in terms of others.
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