Verifiable Ensembles of Low-Dimensional Submodels for Multi-class Problems with Imbalanced Misclassification Costs

摘要

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 inter-pretable 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 interpretabil-ity) of the binary ensemble, and to achieve a good predictive performance.