Combined 5x2cv F Test for Comparing Supervised Classification Learning Algorithms Combined 5x2cv F Test for Comparing Supervised Classification Learning Algorithms
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Dietterich 1] reviews ve statistical tests proposing the 5x2cv t test for determining whether there is a signiicant diierence between the error rates of two classiiers. In our experiments , we noticed that the 5x2cv t test result may vary depending on factors that should not aaect the test and we propose a variant, the combined 5x2cv F test, that combines multiple statistics to get a more robust test. Simulation results show that this combined version of the test has lower Type I error and higher power than 5x2cv proper.
[1] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[2] Thomas G. Dietterich. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.