Combined 5 x 2 cv F test for comparing supervised classification learning algorithms
Combined 5 x 2 cv F test for comparing supervised classification learning algorithms
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DOI：
10.1162/089976699300016007
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发表时间：
1999-11-15
影响因子：
2.9
通讯作者：
Alpaydin, E
中科院分区：
文献类型：
--
作者：
Alpaydin, E
Dietterich（1998）回顾了五种统计检验，并提出了5 × 2 CV t检验来确定两个分类器的错误率之间是否存在显着差异。在我们的实验中，我们注意到5 x 2 cv t检验结果可能会因不应影响检验的因素而异，因此我们提出了一种变体，即组合5 x 2 cv F检验，它结合了多种统计量以获得更稳健的检验。仿真结果表明，这种组合版本的测试具有较低的I型错误和较高的功率比5 × 2 CV适当。
Dietterich (1998) reviews five statistical tests and proposes the 5 x 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 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 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 5 x 2 cv proper.