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学者姓名：黄昊杰
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Abstract ：
In the wastewater treatment process, unsupervised domain adaptation (UDA) enables cross-condition prediction for key performance indicators. However, the lack of interpretability in predictions can compromise the reliability of the model. This work proposes a transferable ensemble additive network (TEAN) that is both capable of solving domain adaptation tasks and providing interpretable predictions. TEAN consists of an ensemble additive network (EAN) and a transferable additive network (TAN), which are essentially a transfer feature learning model and a domain adaptation model based on multikernel maximum mean discrepancy (MK-MMD), respectively. To improve the performance of the model in terms of domain adaptation, the EAN is pretrained to learn transfer features, and the latent feature dimensions in the TAN are augmented to better learn and capture interdomain discrepancies. To enhance interpretability, EAN conducts feature selection based on importance to obtain sparse feature representations. To avoid inconsistent selection results across multiple runs compromising the interpretability of the model, TEAN constructs weighted variance to measure the importance of features and applies an ensemble strategy in building EAN. Experimental results conducted on data generated from benchmark simulation model No. 1 (BSM1) demonstrate that TEAN outperforms the other comparison methods. TEAN achieves more consistent feature selection results under multiple runs and exhibits excellent prediction accuracy for key performance indicators, while its predictions are interpretable.
Keyword ：
Adaptation models Adaptation models Additives Additives Data models Data models Domain adaptation Domain adaptation Feature extraction Feature extraction generalized additive model (GAM) generalized additive model (GAM) interpretable machine learning interpretable machine learning Predictive models Predictive models Representation learning Representation learning Shape Shape Splines (mathematics) Splines (mathematics) transfer learning (TL) transfer learning (TL) Wastewater treatment Wastewater treatment Water resources Water resources
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| GB/T 7714 | Su, Cheng , Peng, Xin , Yang, Dan et al. A Transferable Ensemble Additive Network for Interpretable Prediction of Key Performance Indicators [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Su, Cheng et al. "A Transferable Ensemble Additive Network for Interpretable Prediction of Key Performance Indicators" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Su, Cheng , Peng, Xin , Yang, Dan , Lu, Renzhi , Huang, Haojie , Zhong, Weimin . A Transferable Ensemble Additive Network for Interpretable Prediction of Key Performance Indicators . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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Abstract ：
The presence of outliers in the training data affects the accuracy of the constructed model. To cope with the outlier interference in the model construction process, some robust methods have been proposed on the basis of the nonparametric method, Gaussian process regression (GPR), without eliminating the outliers previously. However, the high complexity of these robust GPR methods makes them unable to cope with situations where the amount of data is too large. In this article, we analyze the impact of outliers on model construction in the setting of big data and propose a robust version based on the sparse GPR. Empirical evaluations conducted on two publicly available datasets, as well as a nitrogen oxides soft sensor designed for a physical diesel engine whose data exist outliers that are difficult to distinguish from normal data, provide compelling evidence to support the notion that the proposed method leads to significant enhancements in performance.
Keyword ：
Big Data Big Data Complexity theory Complexity theory Computational modeling Computational modeling Data models Data models Gaussian processes Gaussian processes Gaussian process regression (GPR) Gaussian process regression (GPR) Kernel Kernel robustness robustness soft sensor soft sensor Soft sensors Soft sensors sparse GPR sparse GPR
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| GB/T 7714 | Huang, Haojie , Peng, Xin , Du, Wei et al. Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Huang, Haojie et al. "Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Huang, Haojie , Peng, Xin , Du, Wei , Zhong, Weimin . Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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Abstract ：
During the process of real-time monitoring, low sampling rate make it difficult to construct a prediction model of variable due to the lack of data. Transfer learning addresses the dilemma of lacking sufficiently labeled data for training neural networks by leveraging relevant labeled data for knowledge transfer, which can significantly improve the prediction accuracy of the low sampling rate variable by utilizing high sampling rate variables. When the feature spaces of low and high sampling rate indicators do not coincide, it constitutes a special case of transfer learning known as Heterogeneous Transfer Learning (HTL). One classical way of HTL is fine-tuning the pre-trained source model. Nevertheless, they mainly focus on optimizing the weights of pre-trained models and ignore the mismatch of structure. Therefore, in this paper, Domain Perceptive-Pruning and Fine-tuning (DP-PF) is proposed for HTL to simultaneously tune the structure and weights of the source pre-trained model and improve its adaptability to the target task. Specifically, DP-PF proposes target-perceptive pruning that removes unimportant layers from the source pre-trained model based on the target pre-trained model to tune structure, importance-perceptive fine-tuning with adaptive learning rate based on layer importance to tune the weights, and source-perceptive regularizing to mitigate catastrophic forgetting of original knowledge contained in the source model. Experiments are constructed based on the wastewater treatment process and air quality prediction. The R-2 predicted by DP-PF is at least 12% higher than that of other compared methods. The excellence of DP-PF in accurate cross-domain prediction proves the effectiveness of the proposed method.
Keyword ：
Air quality prediction Air quality prediction Fine-tune Fine-tune Heterogeneous transfer learning Heterogeneous transfer learning Prune Prune Regularize Regularize Wastewater treatment process Wastewater treatment process
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| GB/T 7714 | Yang, Dan , Peng, Xin , Wu, Xiaolong et al. Domain perceptive-pruning and fine-tuning the pre-trained model for heterogeneous transfer learning in cross domain prediction [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 260 . |
| MLA | Yang, Dan et al. "Domain perceptive-pruning and fine-tuning the pre-trained model for heterogeneous transfer learning in cross domain prediction" . | EXPERT SYSTEMS WITH APPLICATIONS 260 (2024) . |
| APA | Yang, Dan , Peng, Xin , Wu, Xiaolong , Huang, Haojie , Li, Linlin , Zhong, Weimin . Domain perceptive-pruning and fine-tuning the pre-trained model for heterogeneous transfer learning in cross domain prediction . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 260 . |
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