{"type":"ScholarlyArticle","title":"sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)","authors":[{"name":"Tong Liu","slug":"tong-liu","given":"Tong","family":"Liu","affiliation":"Meituan , Beijing , China"},{"name":"Roberto Amadini","slug":"roberto-amadini","given":"Roberto","family":"Amadini","affiliation":"Department of Computer Science and Engineering , University of Bologna , Italy"},{"name":"Maurizio Gabbrielli","slug":"maurizio-gabbrielli","given":"Maurizio","family":"Gabbrielli","affiliation":"Department of Computer Science and Engineering , University of Bologna , Italy"},{"name":"Jacopo Mauro","slug":"jacopo-mauro","given":"Jacopo","family":"Mauro","affiliation":"Department of Mathematics and Computer Science , University of Southern Denmark , Denmark"}],"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.","doi":"10.24963/ijcai.2022/804","url":"https://ijcai.org/proceedings/2022/804","pdf":"https://ijcai.org/proceedings/2022/0804.pdf","markdown":"https://ijcai.org/proceedings/2022/804.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/804","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Journal Track","track":"Journal Track","number":804,"pages":{"first":5752,"last":5756},"keywords":["Machine Learning: Applications","Constraint Satisfaction and Optimization: Constraints and Machine Learning","Machine Learning: Optimisation","Machine Learning: General","Machine Learning: Classification"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987234","length_sec":80,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985377","length_sec":608,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Amadini et al., 2014] Roberto Amadini, Maurizio Gab- brielli, and Jacopo Mauro. 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