2008
JMLR
JMLR 2008
An Extension on “Statistical Comparisons of Classifiers over Multiple Data Sets” for all Pairwise Comparisons
Abstract
In a recently published paper in JMLR, Demšar (2006) recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method. However, it does not deal with some advanced topics in depth. Regarding these topics, we focus on more powerful proposals of statistical procedures for comparing n × n classifiers. Moreover, we illustrate an easy way of obtaining adjusted and comparable p-values in multiple comparison procedures. [abs] [ pdf ][ bib ] © JMLR 2008. (edit, beta)
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Keyword Pioneer
— p-value adjustment
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Hot Topic Early Bird
— pairwise comparison
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— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
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