AS-ASL: Algorithm Selection with Auto-sklearn

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Brandon Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, Petri Myllymäki ;
Proceedings of the Open Algorithm Selection Challenge, PMLR 79:19-22, 2017.

Abstract

In this paper, we describe our algorithm selection with Auto-sklearn (as-asl) software as it was entered in the 2017 Open Algorithm Selection Challenge. as-asl first selects informative sets of features and then uses those to predict distributions of algorithm runtimes. A classifier uses those predictions, as well as the informative features, to select an algorithm for each problem instance. Our source code is publicly available with the permissive MIT license.

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