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Combined 5x2cv $F$-Test for Comparing Supervised Classification Learning Algorithms

Dietterich (1998) reviews five statistical tests proposing the 5x2cv t test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5x2cv t test result may vary depending on factors that should not affect 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.

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