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| Titel: |
Meta-conformity approach to reliable classification |
| Auteur: |
Smirnov, E.N. Nalbantov, G.I. Kaptein, A.M. |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 13 (2009) nr. 6 pagina's 901-915 |
| Jaar: |
2009-11-25 |
| Inhoud: |
The conformity framework has recently been proposed for the task of reliable classification. Given a classifier B, the framework allows to obtain p-values of the classifications assigned to individual instances. However, applying the framework is a difficult problem: we need to construct an instance non-conformity function for the classifierB. To avoid constructing such a function we propose a meta-conformity approach. If a conformity-based classifier M is available, the approach is to train M as a meta classifier that predicts the correctness of each classification of the classifier B. In this way the classification p-values of the classifier B are represented by the classification p-values of the classifier M. The meta-conformity approach can be used for constructing classifiers with predefined generalization performance. Experiments show that the approach results in classifiers that can outperform existing conformity-based classifiers. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 2 van 9 gevonden artikelen
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