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 paper introduces a fuzzy-rule-based classification method called fuzzy round robin [repeated incremental pruning to produce error reduction (RIPPER)] (FR3). As the name suggests, FR3 builds upon the RIPPER algorithm, a state-of-the-art rule learner. More specifically, in the context of polychotomous classification, it uses a fuzzy extension of RIPPER as a base learner within a round robin scheme, and thus, can be seen as a fuzzy variant of the R3 learner that has recently been introduced in the literature. A key feature of FR3, in comparison with its nonfuzzy counterpart, is its ability to represent different facets of uncertainty involved in a classification decision in a more faithful way. FR3 thus provides the basis for implementing “reliable classifiers” that may abstain from a decision when not being sure enough, or at least indicate that a classification is not fully supported by the empirical evidence at hand. Besides, our experimental results show that FR3 outperforms R3 in terms of classification accuracy, and therefore, suggest that it produces predictions that are not only more reliable but also more accurate. The superb classification performance of FR3 is furthermore confirmed by comparing it to other state-of-the-art (fuzzy) rule learners.