An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons

By Salvador Garcia, Francisco Herrera. 2008

1,518 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0f09767a303c2b97807c51996fb53dbbafedf9af

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Citations in the corpus

Enhancing particle swarm optimization using generalized opposition-based learning (2011, methodology): “To compare the performance of multiple algorithms on this test suite, the average ranking of the Friedman test is conducted by the suggestions considered in [12,13].”
Deep learning for time series classification: a review (2018, methodology): “(2016) where the average rank comparison is replaced by a Wilcoxon signed-rank test (Wilcoxon, 1945) with Holm’s alpha correction (Holm, 1979; Garcia and Herrera, 2008).”
Machine learning and deep learning (2021, methodology): “Cross-validation provides the opportunity to compare the reliability of ML models by providing multiple out-of-sample data instances that enable comparative statistical testing (García and Herrera 2008).”
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013, methodology): “These tests are suggested in the studies presented in [11,28,29], where its use in the ﬁeld of machine learning is highly recommended.”
An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, background): “67 (1450/1500) Conducting a statistical test on differences between accuracies is absolutely crucial to evaluate the performance of different classifiers [41, 42].”
Predictive modeling of structured electronic health records for adverse drug event detection (2015, methodology): “To look further at the pairwise significance between the inspected models, a post-hoc test using the Bergman-Hommel procedure was applied [34].”
InceptionTime: Finding AlexNet for time series classification (2019, methodology): “%) correction (Garcia and Herrera, 2008).”
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework (2011, methodology): “1 KEEL Statistical Tests Nowadays, the use of statistical tests to improve the evaluation process of the performance of a new method has become a widespread technique in the field of Data Mining [10, 19, 20].”
HIVE-COTE 2.0: a new meta ensemble for time series classification (2021, methodology): “For multiple classifiers over multiple datasets we use an adaptation of the critical difference diagram (Demšar, 2006), replacing the post-hoc Nemenyi test with a comparison of all classifiers using pairwise Wilcoxon signed-rank tests, and cliques formed using the Holm correction recommended by (Gar”
Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, unknown): “These tests are widely used in the field of machine learning as can be shown in [82, 84, 85, 86], where they recommend their use.”