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- W1597076405 abstract "In this chapter, we discuss different strategies of extending an ensemble approach based on local binary classifiers to solve multi-class problems. The ensembles of binary classifiers were developed with the objective of providing interpretable submodels s for use in safety-related application domains. The ensembles assume highly imbalanced misclassification costs between the two classes. The extension to multi-class problems is not straightforward because common multi-class extensions might induce inconsistent decisions. We propose a solution of this problem that avoids such inconsistencies by introducing a hierarchy of misclassification costs. We show that by following such a hierarchy it becomes feasible to extend the binary ensemble, to maintain the desirable properties (that is, the good interpretability) of the binary ensemble, and to achieve a good predictive performance." @default.
- W1597076405 created "2016-06-24" @default.
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- W1597076405 date "2009-01-01" @default.
- W1597076405 modified "2023-09-26" @default.
- W1597076405 title "Verifiable Ensembles of Low-Dimensional Submodels for Multi-class Problems with Imbalanced Misclassification Costs" @default.
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- W1597076405 doi "https://doi.org/10.1007/978-3-642-03999-7_11" @default.
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