.png){kind=icon}
.png){kind=icon}
Return
Adaptive ensemble learning strategy for semi-supervised soft sensing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jfranklin.2019.07.008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes an adaptive ensemble learning strategy for soft sensor development with semi-supervised learning. The main target of the proposed method is to improve the regression performance with a limited number of labeled samples, under the ensemble learning framework. First, the missing outputs are estimated by the k-nearest neighbor method. In order to improve the accuracy of sub-models for ensemble modeling, a novel sample selection mechanism is established to select the most useful estimated data samples. Second, the Bagging method is employed to both of the labeled and selected datasets, and the two different kinds of datasets are matched based on the Dissimilarity algorithm. As a result, the proposed method enhances the diversity and accuracy of the sub-models which are two important issues for ensemble learning. An industrial case study is carried out to demonstrate the effectiveness of the proposed method in dealing with semi-supervised soft sensing issue. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
SENSOR
DISSIMILARITY
PREDICTION
REGRESSION
SELECTION
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.7
Papers:
6.3K
Citations:
1.5W