The problem of label ranking has recently been introduced as an extension of conventional classification in the field of machine learning. In this paper, we argue that label ranking is an amenable task from a CBR point of view and, in particular, is more amenable to supporting case-based problem solving than standard classification. Moreover, by developing a case-based approach to label ranking, we will show that, the other way round, concepts and techniques from CBR are also useful for label ranking. In addition to an experimental study in which case-based label ranking is compared to conventional nearest neighbor classification, we present an application in which label ranking is used for node ordering in heuristic search
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