Generalization of an outlier model into a “global” perspective

In this work, we study the generalization of an outlier model from two perspectives, temporal and spatial. We show that model generalization with existing distribution-based outlier analysis methods can vary significantly. We then propose a “big data” outlier analysis approach together with a probability-based outlier evaluation for improving model generalization. Experiments are conducted based on two automotive product lines to explain the concepts and demonstrate the effectiveness of the proposed approach.