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|Title:||Distance-Based Decision Tree Algorithms for Label Ranking|
|Authors:||Cláudio Rebelo Sá|
Carlos Manuel Soares
|Abstract:||The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have developed/adapted to treat rankings as the target object follow two different approaches: distribution-based (e.g., using Mallows model) or correlation-based (e.g., using Spearman's rank correlation coefficient). Decision trees have been adapted for label ranking following both approaches. In this paper we evaluate an existing correlation-based approach and propose a new one, Entropy-based Ranking trees. We then compare and discuss the results with a distribution-based approach. The results clearly indicate that both approaches are competitive.|
|Appears in Collections:||CESE - Indexed Articles in Conferences|
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