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This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and… (More)

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… (More)

This paper introduces a fuzzy rule-based classification method called FR3, which is short for Fuzzy Round Robin RIPPER. 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… (More)

In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, the order information of the problem, essentially relying on the assumption that the ordinal structure of the set of class labels is also reflected in the topology of the instance… (More)

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