… Next, we consider an ensemble model where we perform inference over the graph to extract
information on the learned compatibility scores in each base models. Thus, we assume that …

… The methods that are proposed in this paper do not incorporate labels as features during
classification and thus prevent any influence of the features on the inter-label dependencies. …

… An intuitive value for t is 0.5, but RAKEL performs well across a wide range of t values as it
… a new ensemble method for multilabel classification that is based on random projections of …

… In this paper, we extend to the multilabel case the idea of averaging over a constrained
family of classifiers. We assume the graph connecting the classes to be a tree. Rather than …

… focusing on ensemble techniques within a multi-label learner… each classifier are maintained
constant during the classification of … are evaluated in this paper for multi-label classification. …

… We observe that RAkELo provides a higher improvement over LP than RAkELd. This fact …
presented a new multilabel classification method, called RAkEL, that learns an ensemble of LP …

… paper are often superior alternatives to other multi-label methods over a range of multi-labelled …
to many multi-label classification tasks including large and complex multi-label datasets. …

… 8] random subsets of training data are used. The aim of this paper is to use heterogeneous
ensemble of multilabel … focuses on ensemble techniques within a multi-label learner while we …

… We develop a transductive multilabel classifier (TMC) to … called transductive multilabel
ensemble classifier (TMEC) for … function prediction problem within a multilabel learning framework. …

… ML-FOREST, to learn an ensemble of hierarchical multi-label classifier trees to reveal the …
may cause over-fitting issues [11]. In this paper, we propose a new tree ensemble algorithm, …