Efficient ensembles of distance‐based label ranking trees
Enrique G. Rodrigo,
Juan C. Alfaro,
Juan A. Aledo
et al.
Abstract:Ensemble of label ranking trees (LRTs) are currently the state‐of‐the‐art approaches to the label ranking problem. Recently, bagging, boosting, and random forest methods have been proposed, all based on the LRT algorithm, which adapts regression/classification trees to the label classification problem. The LRT algorithm uses theoretically grounded Mallows probability distribution to select the best split when growing the tree, and an EM‐type process to complete the rankings on the training data when they are i… Show more
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