Pairwise meta-rules for better meta-learning-based algorithm ranking
Machine Learning
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Meta-learning, as applied to model selection, consists of inducing mappings from tasks to learners. Traditionally, tasks are characterised by the values of pre-computed meta-attributes, such as statistical and information-theoretic measures, induced decision trees'' characteristics and/or landmarkers'' performances. In this position paper, we propose to (meta-)learn directly from induced decision trees, rather than rely on an \em ad hoc set of pre-computed characteristics. Such meta-learning is possible within the framework of the typed higher-order inductive learning framework we have developed.