Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
نویسندگان
چکیده
منابع مشابه
Approximate Statistical Tests for Comparing Supervised Classi cation Learning Algorithms
This paper reviews ve approximate statistical tests for determining whether one learning algorithm out-performs another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a diierence when no diierence exists (type I error). Two widely-used statistical tests are shown to have high probability of Type I error in certain s...
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This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I error). Two widely used statistical tests are shown to have high probability of type I error in cert...
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Dietterich (1998) reviews five statistical tests and proposes the 5 × 2 cv t test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5× 2 cv t test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5×2 cv F test, that combines multiple statistics ...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 1998
ISSN: 0899-7667,1530-888X
DOI: 10.1162/089976698300017197