Multi-Class Extension of Verifiable Ensemble Models for Safety-Related Applications

Abstract

This contribution discusses two different strategies for extending a verifiable ensemble approach for binary classification tasks to also solve multi-class problems. The binary ensemble approach was developed with the objective of providing interpretable classification models for use in safety-related application domains. It is based on low-dimensional submodels. Each submodel uses only a low-dimensional subspace of the complete input space facilitating the visual interpretation and validation by domain experts. Thus, the correct inter- and extrapolation behavior can be guaranteed. The extension to multi-class problems is not straightforward because common multi-class extensions might induce inconsistent decisions. The proposed approaches avoid such inconsistencies by introducing a hierarchy of misclassification costs. We will show that by following such a hierarchy the extension of the binary ensemble becomes feasible and the desirable properties of the binary classification approach for safety-related problems can be maintained.