AS-ASL: Algorithm Selection with Auto-sklearn

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v79-malone17a, title = {AS-ASL: Algorithm Selection with Auto-sklearn}, author = {Malone, Brandon and Kangas, Kustaa and Järvisalo, Matti and Koivisto, Mikko and Myllymäki, Petri}, booktitle = {Proceedings of the Open Algorithm Selection Challenge}, pages = {19--22}, year = {2017}, editor = {Lindauer, Marius and van Rijn, Jan N. and Kotthoff, Lars}, volume = {79}, series = {Proceedings of Machine Learning Research}, month = {11--12 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v79/malone17a/malone17a.pdf}, url = {https://proceedings.mlr.press/v79/malone17a.html}, 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.} }
Endnote
%0 Conference Paper %T AS-ASL: Algorithm Selection with Auto-sklearn %A Brandon Malone %A Kustaa Kangas %A Matti Järvisalo %A Mikko Koivisto %A Petri Myllymäki %B Proceedings of the Open Algorithm Selection Challenge %C Proceedings of Machine Learning Research %D 2017 %E Marius Lindauer %E Jan N. van Rijn %E Lars Kotthoff %F pmlr-v79-malone17a %I PMLR %P 19--22 %U https://proceedings.mlr.press/v79/malone17a.html %V 79 %X 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.
APA
Malone, B., Kangas, K., Järvisalo, M., Koivisto, M. & Myllymäki, P.. (2017). AS-ASL: Algorithm Selection with Auto-sklearn. Proceedings of the Open Algorithm Selection Challenge, in Proceedings of Machine Learning Research 79:19-22 Available from https://proceedings.mlr.press/v79/malone17a.html.

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