The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Complexity measures of supervised classification problems
Abstract
We studied a number of measures that characterize the difficulty of a classification problem, focusing on the geometrical complexity of the class boundary. We compared a set of real-world problems to random labelings of points and found that real problems contain structures in this measurement space that are significantly different from the random sets. Distributions of problems in this space show that there exist at least two independent factors affecting a problem's difficulty. We suggest using this space to describe a classifier's domain of competence. This can guide static and dynamic selection of classifiers for specific problems as well as subproblems formed by confinement, projection, and transformations of the feature vectors.
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Metadata
- Authors
- Ho, T. K., & Basu, M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-03
- Volume
- 24
- Issue
- 3
- Pages
- 289-300
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2125877832
- Cited by
- 734
- References
- 20
- FWCI
- 9.03
- Type
- journal article
- Language
- en
- Open access
- closed
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