Representational Issues in Meta-Learning

Alexandros Kalousis and Melanie Hilario

To address the problem of algorithm selection for the classification task, we equip a relational case base with new similarity measures that are able to cope with multirelational representations. The proposed approach builds on notions from clustering and is closely related to ideas developed in similarity-based relational learning. The results provide evidence that the relational representation coupled with the appropriate similarity measure can improve performance. The ideas presented are pertinent not only for meta-learning representational issues, but for all domains with similar representation requirements.


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