Active learning based semi-supervised exponential discriminant analysis and its application for fault classification in industrial processes

Abstract

For the industrial fault classification, exponential discriminant analysis (EDA) requires that all the training samples should be labeled; however, only a minority of the training samples are randomly labeled in real industrial processes. This motivates the formulation of the active learning based semi-supervised exponential discriminant analysis in this paper. Firstly, to make EDA applicable to the semi-supervised industrial scenario, scatter matrices are transformed into their regularization variants through combining unlabeled training samples. Moreover, to reduce the adverse effect of random labeling of training samples, the best versus second-best rule is employed to select more informative training samples in an active way to upgrade the model classification performance. And the obvious performance improvement of the proposed method is demonstrated with extensive experiments on synthesized data, the TE process and a real industrial process.