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In recent years, the usage of ensemble learning in applications has grownsignificantly due to increasing computational power allowing the training oflarge ensembles in reasonable time frames. Many applications, e.g., malwaredetection, face recognition, or financial decision-making, use a finite set oflearning algorithms and do aggregate them in a way that a better predictiveperformance is obtained than any other of the individual learning algorithms.In the field of Post-Silicon Validation for semiconductor devices (PSV), datasets are typically provided that consist of various devices like, e.g., chipsof different manufacturing lines. In PSV, the task is to approximate theunderlying function of the data with multiple learning algorithms, each trainedon a device-specific subset, instead of improving the performance of arbitraryclassifiers on the entire data set. Furthermore, the expectation is that anunknown number of subsets describe functions showing very differentcharacteristics. Corresponding ensemble members, which are called outliers, canheavily influence the approximation. Our method aims to find a suitableapproximation that is robust to outliers and represents the best or worst casein a way that will apply to as many types as possible. A 'soft-max' or'soft-min' function is used in place of a maximum or minimum operator. A NeuralNetwork (NN) is trained to learn this 'soft-function' in a two-stage process.First, we select a subset of ensemble members that is representative of thebest or worst case. Second, we combine these members and define a weightingthat uses the properties of the Local Outlier Factor (LOF) to increase theinfluence of non-outliers and to decrease outliers. The weighting ensuresrobustness to outliers and makes sure that approximations are suitable for mosttypes.

ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\