Meta-Feature Taxonomy for Supporting Automatic Machine Learning

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Many automatic machine learning (AutoML) libraries have been developed recently, meeting public demand for more machine learning tools which can be used without an expert. A common tactic illicited by these frameworks is to initially generate meta-features which are then used as an initial heuristic for further evaluation in recent AutoML frameworks. In this thesis we provide a systematic categorization of meta-features in the AutoML literature. Current implementations of automatic machine learning frameworks fail to provide reasoning for meta-feature selection, and a taxonomic categorization is needed. Our approach reviewed current AutoML frameworks and created a taxonomy of five categories into which any meta-feature can be categorized. We have created a general framework with which any currently used meta-features can be described, as well as demonstrate some scenarios for their applications. Additionally, a runtime analysis of the wall-clock time required for meta-feature generation is provided for 18 data collections found in previous CHALearn AutoML competitions, which took between 0:10:26.9, and 98:43:46.5. Additionally we found that a sample percentage of 0.1 is sufficient for use in Sample Variant Landmark Meta-Feature generation when using the Nearest Neighbour, Elite Nearest Neighbour, Best Decision Node, and Random Decision Node Landmarks which indicates potential use as meta-features in AutoML.
Machine Learning, Meta-Feature, Automatic Machine Learning, AutoML
Cooper T.S. Meta-Feature Taxonomy for Supporting Automatic Machine Learning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from