Cost-conscious comparison of supervised learning algorithms over multiple data sets
Yükleniyor...
Tarih
2012-04
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from "best" to "worst" where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi2Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms.
Açıklama
Anahtar Kelimeler
Machine learning, Statistical tests, Classifier comparison, Model selection, Model complexity, Statistical comparisons, Classification, Tests, Classifiers, Computer simulation, Supervised learning, Generalization error, Machine-learning, Multiple data, Multiple learning algorithms, Space complexity, Learning algorithms
Kaynak
Pattern Recognition
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
45
Sayı
4
Künye
Ulaş, A., Yıldz, O. T. & Alpaydın, A. İ. E. (2012). Cost-conscious comparison of supervised learning algorithms over multiple data sets. Pattern Recognition, 45(4), 1772-1781. doi:10.1016/j.patcog.2011.10.005
