Active matrix completion for algorithm selection

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dc.authoridMustafa Mısır / 0000-0002-6885-6775en_US
dc.authorscopusidMustafa Mısır / 36458858100
dc.authorwosidMustafa Mısır / A-6739-2010
dc.contributor.authorMısır, Mustafa
dc.date.accessioned2020-08-30T20:01:36Z
dc.date.available2020-08-30T20:01:36Z
dc.date.issued2019
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019 -- 10 September 2019 through 13 September 2019 -- -- 236069en_US
dc.descriptionMısır , Mustafa (isu author)
dc.description.abstractThe present work accommodates active matrix completion to generate cheap and informative incomplete algorithm selection datasets. Algorithm selection is being used to detect the best possible algorithm(s) for a given problem ((formula presented) instance). Although its success has been shown in varying problem domains, the performance of an algorithm selection technique heavily depends on the quality of the existing dataset. One critical and likely to be the most expensive part of an algorithm selection dataset is its performance data. Performance data involves the performance of a group of algorithms on a set of instance of a particular problem. Thus, matrix completion [1] has been studied to be able to perform algorithm selection when the performance data is incomplete. The focus of this study is to come up with a strategy to generate/sample low-cost, incomplete performance data that can lead to effective completion results. For this purpose, a number of matrix completion methods are utilized in the form of active matrix completion. The empirical analysis carried out on a set of algorithm selection datasets revealed significant gains in terms of the computation time, required to produce the relevant performance data. © Springer Nature Switzerland AG 2019.en_US
dc.description.sponsorshipEuropean Cooperation in Science and Technology: CA15140en_US
dc.description.sponsorshipAcknowledgement. This study was partially supported by an ITC Conference Grant from the COST Action CA15140.en_US
dc.identifier.citationMısır, M. (2019, September). Active Matrix Completion for Algorithm Selection. In International Conference on Machine Learning, Optimization, and Data Science (pp. 321-334). Springer, Cham.en_US
dc.identifier.doi10.1007/978-3-030-37599-7_27en_US
dc.identifier.endpage334en_US
dc.identifier.isbn9.78303E+12
dc.identifier.issn0302-9743en_US
dc.identifier.scopus2-s2.0-85078472934en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage321en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-37599-7_27
dc.identifier.urihttps://hdl.handle.net/20.500.12713/313
dc.identifier.volume11943 LNCSen_US
dc.identifier.wosWOS:000654942500027en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMısır, Mustafaen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleActive matrix completion for algorithm selectionen_US
dc.typeConference Objecten_US

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