PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Benchmarking Non Parametric Statistical Tests
Mikaela Keller, Samy Bengio and Siew Yeung Wong
In: Advances in Neural Information Processing Systems, NIPS 18(2005).

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.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1300
Deposited By:Samy Bengio
Deposited On:28 November 2005