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| Titel: |
A reduction technique for nearest-neighbor classification: Small groups of examples |
| Auteur: |
Miroslav Kubat Martin Cooperson, Jr. |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 5 (2002) nr. 6 pagina's 463-476 |
| Jaar: |
2002-01-15 |
| Inhoud: |
An important issue in nearest-neighbor classifiers is how to reduce the size of large sets of examples. Whereas many researchers recommend to replace the original set with a carefully selected subset, we investigate a mechanism that creates three or more such subsets. The idea is to make sure that each of them, when used as a 1-NN subclassifier, tends to err in a different part of the instance space. In this case, failures of individuals can be corrected by voting. The costs of our example-selection procedure are linear in the size of the original training set and, as our experiments demonstrate, dramatic data reduction can be achieved without a major drop in classification accuracy. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 3 van 6 gevonden artikelen
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