Abstract:Although machine learning algorithms have been successful when applied to several tasks, the selection of the most suitable for a given dataset is not straightforward. The recommendation of machine learning algorithms can be automated through the use of meta-learning, but this requires efficient methods for the characterizations of datasets, i.e. meta-features extraction. In this work we propose to accelerate the extraction of clustering-based meta-features on GPUs, taking advantage of the optimized libraries … Show more
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