Interpretable ensembles of local models for safety-related applications

Abstract. This paper discusses a machine learning approach for binary classification problems which satisfies the specific requirements of safety-related applications. The approach is based on ensembles of local models. Each local model utilizes only a small subspace of the complete input space. This ensures the interpretability and verifiability of the local models, which is a crucial prerequisite for applications in safety-related domains. A feature construction method based on a multi-layer perceptron architecture is proposed to overcome limitations of the local modeling strategy, while keeping the global model interpretable.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Sebastian Nusser, Clemens Otte, Werner Hauptmann
• Keywords: ESANN 2008, Local Models, Machine Learning Approach, Neural Networks, Safety-related Applications