288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

January 2014, Vol. 26, No. 1, Pages 208-235
(doi: 10.1162/NECO_a_00532)
© 2013 Massachusetts Institute of Technology
Blocked 3×2 Cross-Validated t-Test for Comparing Supervised Classification Learning Algorithms
Article PDF (846 KB)

In the research of machine learning algorithms for classification tasks, the comparison of the performances of algorithms is extremely important, and a statistical test of significance for generalization error is often used to perform it in the machine learning literature. In view of the randomness of partitions in cross-validation, a new blocked 3×2 cross-validation is proposed to estimate generalization error in this letter. We then conduct an analysis of variance of the blocked 3×2 cross-validated estimator. A relatively conservative variance estimator that considers the correlation between any two two-fold cross-validations, and was previously neglected in 5×2 cross-validated t and F-tests is put forward. A corresponding test using this variance estimator is presented to compare the performances of algorithms. Simulated results show that the performance of our test is comparable with that of 5×2 cross-validated tests but with less computation complexity.