Mapping classifiers and datasets
Yükleniyor...
Tarih
2011-04
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Given the posterior probability estimates of 14 classifiers on 38 datasets, we plot two-dimensional maps of classifiers and datasets using principal component analysis (PCA) and Isomap. The similarity between classifiers indicate correlation (or diversity) between them and can be used in deciding whether to include both in an ensemble. Similarly, datasets which are too similar need not both be used in a general comparison experiment. The results show that (i) most of the datasets (approximately two third) we used are similar to each other, (ii) multilayer perceptrons and k-nearest neighbor variants are more similar to each other than support vector machine and decision tree variants. (iii) the number of classes and the sample size has an effect on similarity.
Açıklama
Anahtar Kelimeler
Classifiers, Datasets, No free lunch theorem, PCA, Isomap, Learning algorithms, Classification (of information), Learning systems, Complexity measures, Data sets, K-nearest neighbors, Multi-layer perceptrons, Posterior probability, Sample sizes, Two-dimensional map, Decision trees, Pattern recognition systems, Principal component analysis, Probability, Classifiers
Kaynak
Expert Systems With Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
38
Sayı
4
Künye
Yıldız, O. T. (2011). Mapping classifiers and datasets. Expert Systems with Applications, 38(4), 3697-3702. doi:10.1016/j.eswa.2010.09.027
