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Learning Meta-features for AutoML

Abstract : This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed meta-features with the space of distributions on the hyper-parameter configurations. MetaBu meta-features, learned once and for all, induce a topology on the set of datasets that is exploited to define a distribution of promising hyper-parameter configurations amenable to AutoML. Experiments on the OpenML CC-18 benchmark demonstrate that using MetaBu meta-features boosts the performance of state of the art AutoML systems, (Feurer et al. 2015) and Probabilistic Matrix Factorization (Fusi et al. 2018). Furthermore, the inspection of MetaBu meta-features gives some hints into when an ML algorithm does well. Finally, the topology based on MetaBu meta-features enables to estimate the intrinsic dimensionality of the OpenML benchmark w.r.t. a given ML algorithm or pipeline.
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https://hal.inria.fr/hal-03583789
Contributor : Herilalaina Rakotoarison Connect in order to contact the contributor
Submitted on : Thursday, March 17, 2022 - 11:05:11 AM
Last modification on : Saturday, March 19, 2022 - 3:33:52 AM

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  • HAL Id : hal-03583789, version 2

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Herilalaina Rakotoarison, Louisot Milijaona, Andry Rasoanaivo, Michèle Sebag, Marc Schoenauer. Learning Meta-features for AutoML. ICLR 2022 - International Conference on Learning Representations, Apr 2022, Virtual, United States. ⟨hal-03583789v2⟩

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