machine learning, learning algorithm, permutation test, p-value
The paired-difference t-test is commonly used in the machine learning community to determine whether one learning algorithm is better than another on a given learning task. This paper suggests the use of the permutation test instead hecause it calculates the exact p-value instead of an estimate. The permutation test is also distribution free and the time complexity is trivial for the commonly used 10-fold cross-validation paired-difference test. Results of experiments on real-world problems suggest it is not uncommon to see the t-test estimate deviate up to 30-50% from the exact p-value.
Original Publication Citation
Menke, J., and Martinez, T. R., "Using Permutations Instead of Student's t Distribution for pvalues in Paired-Difference Algorithm Comparisons", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'4, pp. 1331-1336, 24.
BYU ScholarsArchive Citation
Martinez, Tony R. and Menke, Joshua, "Using Permutations Instead of Student’s t Distribution for p-values in Paired-Difference Algorithm Comparisons" (2004). Faculty Publications. 1032.
Physical and Mathematical Sciences
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