Representational issues in meta-learning
Alexandros Kalousis, Mélanie Hilario · 2003
To address the problem of algorithm se-lection for the classification task, we equip a relational case base with new similarity measures that are able to cope with multi-relational representations. The proposed approach builds on notions from clustering and is closely related to ideas developed in similarity-based relational learning. The re-sults 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 re-quirements. 1.