Abstract Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performance. We present extensive experimental results to demonstrate that our new method achieves the highly competitive performance compared with state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information.
Besides the reduction approaches and probabilistic approaches, tree-based approaches are also very popular in label ranking. Several label ranking methods based on decision tree were designed for label ranking. For example, , proposed the first adaptation of decision tree algorithm for label ranking, called label ranking tree (LRT) @cite_9 . A new version of decision trees for label ranking called entropy-based ranking tree (ERT) and a label ranking forest using this ERT as base learner were developed @cite_21 . Recently, a bagging algorithm which takes the weak LRT-based models as base classifiers was proposed. Experimental results show that bagging these weak learners improves not only the LRT algorithm, but also the instances-based algorithms @cite_25 . Actually, our proposed random forest for label ranking (LR-RF) in this work falls this category. In the Section , we experimentally compare our LR-RF with the state-of-the-art algorithms from these categories.
@cite_9 @cite_21 @cite_25
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The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings. This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID EEA 50014 2013. Abstract Preference learning is the branch of machine learning in charge of inducing preference models from data. In this paper we focus on the task known as label ranking problem , whose goal is to predict a ranking among the different labels the class variable can take. Our contribution is twofold: (i) taking as basis the tree-based algorithm LRT described in [1], we design weaker tree-based models which can be learnt more efficiently; and (ii) we show that bagging these weak learners improves not only the LRT algorithm, but also the state-of-the-art one (IBLR [1]). Furthermore, the bagging algorithm which takes the weak LRT-based models as base classifiers is competitive in time with respect to LRT and IBLR methods. To check the goodness of our proposal, we conduct a broad experimental study over the standard benchmark used in the label ranking problem literature.