Combined 5 x 2 cv F test for comparing supervised classification learning algorithms

Dietterich (1998) reviews five statistical tests and proposes the 5 x 2 cv t test for determining whether there is a significant difference between th e error rates of two classifiers. In our experiments, we noticed that the 5 x 2 cv t test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5 x 2 cv F test, that comb ines 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 p ower than 5 x 2 cv proper.