2009 Ninth IEEE International Conference on Data Mining Permutation Tests for Studying Classifier Performance

Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “Machine learning and statistics literature is replete with more intricate forms of these tests (Ojala and Garriga, 2010)(Nichols and Holmes, 2002).”
Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. (2012, background): “Some researchers have explored a framework of permutation tests for assessing classifier performance (Golland and Fischl, 2003; Ojala and Garriga, 2010).”
Minimax optimality of permutation tests (2020, methodology): “Due to this attractive non-asymptotic property, the permutation test has received considerable attention and has been applied to a wide range of statistical tasks including testing independence, two-sample testing, change point detection, clustering, classification, principal component analysis (see”
Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI (2019, methodology): “We assessed statistical significance of the presented SNCAEs classifiers performance using permutation testing [55].”
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites (2016, methodology): “In order to assess the above-chance accuracy performance, we run a permutation test [42] shuffling labels of both training and test sets at each repetition (1000 permutations).”
Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review (2020, background): “[34] Permutational - Classification Presentation of two permutational tests that study if the algorithm has learnt the data structure and if it uses the attributes distribution and dependencies.”
Ten quick tips for machine learning in computational biology (2017, methodology): “Other useful techniques to assess the statistical significance of a machine learning predictions are permutation testing [44] and bootstrapping [45].”