Sunny-as2: Enhancing SUNNY for algorithm selection (extended abstract)

Tong Liu, Roberto Amadini*, Maurizio Gabbrielli, Jacopo Mauro*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

SUNNY is a k-nearest neighbors based Algorithm Selection (AS) approach that schedules and runs a number of solvers for a given unforeseen problem. In this work we present sunny-as2, an enhancement of SUNNY for generic AS scenarios that advances the original approach with wrapper-based feature selection, neighborhood-size configuration and a greedy approach to speed-up the training phase. Empirical evidence shows that sunny-as2 is competitive w.r.t. state-of-the-art AS approaches.

Original languageEnglish
Title of host publicationProceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
EditorsLuc De Raedt
PublisherInternational Joint Conferences on Artificial Intelligence
Publication date2022
Pages5752-5756
ISBN (Electronic)9781956792003
DOIs
Publication statusPublished - 2022
Event31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria
Duration: 23. Jul 202229. Jul 2022

Conference

Conference31st International Joint Conference on Artificial Intelligence, IJCAI 2022
Country/TerritoryAustria
CityVienna
Period23/07/202229/07/2022
SponsorArtificial Intelligence Journal, Didi Chuxing, et al., FinVolution Group, International Joint Conferences on Artificial Intelligence (IJCAI), Shanghai Artificial Intelligence Industry Association
SeriesIJCAI International Joint Conference on Artificial Intelligence
ISSN1045-0823

Bibliographical note

Publisher Copyright:
© 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

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