Improving the System Identification of Transonic Wind Tunnel by a Regression Ensemble-Based Outlier Mining Method

In transonic wind tunnel, anomalous data that are often referred to as outliers or anomalies have severe impact on system identification. To address such a problem, outliers should be detected and new substitutions should be provided before system identification. The combined request for outlier detection and compensation makes it suitable to develop a regression-based outlier mining algorithm. To enhance the effectiveness of traditional regression-based algorithm, this paper proposes a novel one based on ensemble learning. In our outlier ensemble, the base regression models are learnt on a two-level ensemble structure. The aim of the first level is to enhance the robustness to unknown outliers by homogeneous ensemble. The goal of the second level is to improve the robustness to base regression model. In order to verify the effectiveness of the proposed hybrid outlier ensemble, we use several real-world datasets from transonic wind tunnel and compare it with several underlying competitors. The experimental results have shown that the proposed outlier ensembles could outperform its competitors with respect to both outlier mining and the improvement of system identification.