New top-down methods using SVMs for Hierarchical Multilabel Classification problems

Hierarchical Multilabel Classification is a problem where the classes involved are hierarchically structured, and examples can be assigned to more than one class simultaneously at a same hierarchical level. This paper describes and evaluates five different methods for this classification task, based on two approaches, named top-down and one-shot. In the top-down approach, the classification task is carried out by discriminating the classes, level by level, in the hierarchy. In the one-shot approach, the methods consider the whole set of classes at once in the classification. Based on the top-down approach, two new hierarchical methods (with label combination and with label decomposition) and the well-known binary hierarchical method are investigated using SVM classifiers. Other two methods from the literature, named HC4.5 and Clus-HMC, based on the one-shot approach, are also used. The methods are applied to ten biological datasets and evaluated using specific metrics for this kind of classification. The experimental results showed that the proposed methods can improve the classification accuracy.