Outlier detection based on a dynamic ensemble model: Applied to process monitoring

This paper focuses on outlier detection and its application to process monitoring. The main contribution is that we propose a dynamic ensemble detection model, of which one-class classifiers are used as base learners. Developing a dynamic ensemble model for one-class classification is challenging due to the absence of labeled training samples. To this end, we propose a procedure that can generate pseudo outliers, prior to which we transform outputs of all base classifiers to the form of probability. Then we use a probabilistic model to evaluate competence of all base classifiers. Friedman test along with Nemenyi test are used together to construct a switching mechanism. This is used for determining whether one classifier should be nominated to make the decision or a fusion method should be applied instead. Extensive experiments are carried out on 20 data sets and an industrial application to verify the effectiveness of the proposed method.