Label Ranking Forests Cláudio Rebelo Sá en Carlos Manuel Soares en Knobbe,A en Cortez,P en 2017-12-12T15:59:37Z 2017-12-12T15:59:37Z 2017 en
dc.description.abstract The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have been developed/adapted to treat rankings of a fixed set of labels as the target object, including several different types of decision trees (DT). One DT-based algorithm, which has been very successful in other tasks but which has not been adapted for label ranking is the Random Forests (RF) algorithm. RFs are an ensemble learning method that combines different trees obtained using different randomization techniques. In this work, we propose an ensemble of decision trees for Label Ranking, based on Random Forests, which we refer to as Label Ranking Forests (LRF). Two different algorithms that learn DT for label ranking are used to obtain the trees. We then compare and discuss the results of LRF with standalone decision tree approaches. The results indicate that the method is highly competitive. en
dc.identifier.uri en
dc.language eng en
dc.relation 5527 en
dc.relation 5001 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Label Ranking Forests en
dc.type article en
dc.type Publication en
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