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 extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.
This paper introduces a fuzzy rulebased classification method called FR3, which is short for Fuzzy Round Robin RIPPER. In the context of polychotomous classification, it uses a fuzzy extension of the well-known RIPPER algorithm as a base learner within a round robin scheme. A key feature of FR3 is its ability to represent different facets of uncertainty involved in a classification decision in a more faithful way, thereby providing the basis for implementing "reliable classifiers" that may, for example, abstain from a decision when not being sure enough.
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 space. The purpose of this paper is to investigate, on an experimental basis, the validity of this assumption. Moreover, we seek to answer the question to what extent existing techniques and learning algorithms for ordinal classification are able to exploit order information, and which properties of these techniques are important in this regard.
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