Advanced Ensemble Learning Strategy Based Semi-supervised Soft Sensing Method
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机译:基于高级集成学习策略的半监督软传感方法
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摘要
The present disclosure provides a novel advanced ensemble learning strategy for soft sensor development with semi-supervised model. The main target of the soft sensor is to improve the prediction performance with a limited number of labeled data samples, under the ensemble learning framework. Firstly, in order to improve the prediction accuracy of sub-models for ensemble modeling, a novel sample selection mechanism is established to select the most significantly estimated data samples. Secondly, the Bagging method is employed to both of the labeled and selected data-set, and the two different kinds of datasets are matched based on the Dissimilarity (DISSIM) algorithm. As a result, the proposed method guarantees the diversity and accuracy of the sub-models which are two significant issues of the ensemble learning. In this work, the soft sensor is constructed upon the Gaussian Process Regression (GPR) model.
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