Journal
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
Volume 80, Issue 1, Pages 88-100Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcss.2013.03.009
Keywords
Cross validation; Statistical comparisons of algorithms; Tests for interval-valued data
Funding
- Spanish Ministry of Economy and Competitiveness [TIN2011-24302]
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A new bootstrap test is introduced that allows for assessing the significance of the differences between stochastic algorithms in a cross-validation with repeated folds experimental setup. Intervals are used for modeling the variability of the data that can be attributed to the repetition of learning and testing stages over the same folds in cross validation. Numerical experiments are provided that support the following three claims: (1) Bootstrap tests can be more powerful than ANOVA or Friedman test for comparing multiple classifiers. (2) In the presence of outliers, interval-valued bootstrap tests achieve a better discrimination between stochastic algorithms than nonparametric tests. (3) Choosing ANOVA, Friedman or Bootstrap can produce different conclusions in experiments involving actual data from machine learning tasks. (C) 2013 Elsevier Inc. All rights reserved.
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