An up-to-date comparison of state-of-the-art classification algorithms

•Up-to-date report on the accuracy and efficiency of state-of-the-art classifiers.•We compare the accuracy of 11 classification algorithms pairwise and groupwise.•We examine separately the training, parameter-tuning, and testing time.•GBDT and Random Forests yield highest accuracy, outperforming SVM...

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Vydané v:	Expert systems with applications Ročník 82; s. 128 - 150
Hlavní autori:	Zhang, Chongsheng, Liu, Changchang, Zhang, Xiangliang, Almpanidis, George
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 01.10.2017 Elsevier BV
Predmet:	Algorithms Classification Classification benchmarking Classifier comparison Classifier evaluation Classifiers Datasets Efficiency Machine learning Neural networks Repositories Run time (computers) Support vector machines Training Classifier comparison Classifier evaluation Classification benchmarking
ISSN:	0957-4174, 1873-6793
On-line prístup:	Získať plný text
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