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Benchmarking Non-Parametric Statistical Tests
Type of publication: Conference paper
Citation: keller:nips:2005
Booktitle: Advances in Neural Information Processing Systems, NIPS 18. MIT Press
Year: 2005
Note: IDIAP-RR 05-38
Crossref: keller:rr05-38:
Abstract: Although non-parametric tests have already been proposed for that purpose, statistical significance tests for non-standard measures (different from the classification error) are less often used in the literature. This paper is an attempt at empirically verifying how these tests compare with more classical tests, on various conditions. More precisely, using a very large dataset to estimate the whole ``population'', we analyzed the behavior of several statistical test, varying the class unbalance, the compared models, the performance measure, and the sample size. The main result is that providing big enough evaluation sets non-parametric tests are relatively reliable in all conditions.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Keller, Mikaela
Bengio, Samy
Wong, Siew Yeung
Added by: [UNK]
Total mark: 0
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