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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Bibliographic Details
Published in:	Expert systems with applications Vol. 82; pp. 128 - 150
Main Authors:	Zhang, Chongsheng, Liu, Changchang, Zhang, Xiangliang, Almpanidis, George
Format:	Journal Article
Language:	English
Published:	New York Elsevier Ltd 01.10.2017 Elsevier BV
Subjects:	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
Online Access:	Get full text
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