The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Permutation Tests for Studying Classifier Performance
Abstract
We explore the framework of permutation-based p-values for assessing the behavior of the classification error. In this paper we study two simple permutation tests. The first test estimates the null distribution by permuting the labels in the data; this has been used extensively in classification problems in computational biology. The second test produces permutations of the features within classes, inspired by restricted randomization techniques traditionally used in statistics. We study the properties of these tests and present an extensive empirical evaluation on real and synthetic data. Our analysis shows that studying the classification error via permutation tests is effective; in particular, the restricted permutation test clearly reveals whether the classifier exploits the interdependency between the features in the data.
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Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., & Hu, D. (2012) · Brain · doi:10.1093/brain/aws059 · Cited by 748
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Metadata
- Authors
- Ojala, M., & Garriga, G. C.
- Venue
- 2009 Ninth IEEE International Conference on Data Mining
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 908-913
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2160331326
- Cited by
- 555
- References
- 24
- FWCI
- 1.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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