An up-to-date comparison of state-of-the-art classification algorithms
作者：
Highlights：
• 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.
• GBDT is the fastest in testing, Naive Bayes the fastest in training.
摘要
•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.•GBDT is the fastest in testing, Naive Bayes the fastest in training.
论文关键词：Classification benchmarking,Classifier comparison,Classifier evaluation
论文评审过程：Received 10 December 2016, Revised 1 April 2017, Accepted 2 April 2017, Available online 5 April 2017, Version of Record 19 April 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.003
