Active matrix completion for algorithm selection
Yükleniyor...
Dosyalar
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019 -- 10 September 2019 through 13 September 2019 -- -- 236069
Mısır , Mustafa (isu author)
Mısır , Mustafa (isu author)
Anahtar Kelimeler
Kaynak
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
11943 LNCS
Sayı
Künye
Mısır, M. (2019, September). Active Matrix Completion for Algorithm Selection. In International Conference on Machine Learning, Optimization, and Data Science (pp. 321-334). Springer, Cham.
