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Im Bereich sicherheitsrelevanter Anwendungen ist es besonders wichtig sicherzustellen, dass die gelernte Lösung im gesamten Eingaberaum korrekt ist. Es wird eine Methode zum Lernen von Klassifikationsmodellen vorgestellt, die auch bei sicherheitskritischen Steuerungsaufgaben einsetzbar ist. Hierfür wird ein Ensemble von niedrigdimensionalen Teilmodellen erzeugt, bei dem jedes Teilmodell von einem Experten interpretiert und validiert werden kann.In the area of safety-related applications, it is particularly important to make sure that the learned solution is correct within the complete input space. A method for learning classification models is introduced which can also be used for safety-critical control functions. An ensemble of low dimensional submodels is built where each submodel can be interpreted and validated by a domain expert.

This chapter describes a machine learning approach for classification problems in safety-related domains. The proposed method is based on ensembles of low-dimensional submodels. The usage of low-dimensional submodels enables the domain experts to understand the mechanisms of the learned solution. Due to the limited dimensionality of the submodels each individual model can be visualized and can thus be interpreted and validated according to the domain knowledge. The ensemble of all submodels overcomes the limited predictive performance of each single submodel while the overall solution remains interpretable and verifiable. By different examples from real-world applications the authors will show that their classification approach is applicable to a wide range of classification problems in the field of safety-related applications - ranging from decision support systems over plant monitoring and diagnosis systems to control tasks with very high safety requirements.