{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:22:34Z","timestamp":1761582154914,"version":"build-2065373602"},"reference-count":84,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,19]],"date-time":"2021-12-19T00:00:00Z","timestamp":1639872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62163019, 61763020 and 61863020"],"award-info":[{"award-number":["62163019, 61763020 and 61863020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Applied Basic Research Project of Yunnan Province","award":["202101AT070096"],"award-info":[{"award-number":["202101AT070096"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes. However, labeled data are often scarce in many real-world applications, which poses a significant challenge when building accurate soft sensor models. Therefore, this paper proposes a novel semi-supervised soft sensor method, referred to as ensemble semi-supervised negative correlation learning extreme learning machine (EnSSNCLELM), for industrial processes with limited labeled data. First, an improved supervised regression algorithm called NCLELM is developed, by integrating the philosophy of negative correlation learning into extreme learning machine (ELM). Then, with NCLELM as the base learning technique, a multi-learner pseudo-labeling optimization approach is proposed, by converting the estimation of pseudo labels as an explicit optimization problem, in order to obtain high-confidence pseudo-labeled data. Furthermore, a set of diverse semi-supervised NCLELM models (SSNCLELM) are developed from different enlarged labeled sets, which are obtained by combining the labeled and pseudo-labeled training data. Finally, those SSNCLELM models whose prediction accuracies were not worse than their supervised counterparts were combined using a stacking strategy. The proposed method can not only exploit both labeled and unlabeled data, but also combine the merits of semi-supervised and ensemble learning paradigms, thereby providing superior predictions over traditional supervised and semi-supervised soft sensor methods. The effectiveness and superiority of the proposed method were demonstrated through two chemical applications.<\/jats:p>","DOI":"10.3390\/s21248471","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"8471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry"],"prefix":"10.3390","volume":"21","author":[{"given":"Youwei","family":"Li","sequence":"first","affiliation":[{"name":"Yunnan Key Laboratory of Computer Technologies Application, Kunming 650500, China"},{"name":"Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2627-431X","authenticated-orcid":false,"given":"Huaiping","family":"Jin","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Computer Technologies Application, Kunming 650500, China"},{"name":"Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shoulong","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Yang","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Computer Technologies Application, Kunming 650500, China"},{"name":"Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangguang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1002\/aic.690180323","article-title":"The use of secondary measurements to improve control","volume":"18","author":"Weber","year":"1972","journal-title":"AIChE J."},{"key":"ref_2","unstructured":"Fortuna, L., Graziani, S., Rizzo, A., and Xibilia, M.G. (2007). Soft Sensors for Monitoring and Control of Industrial Processes, Springer Science & Business Media."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.compchemeng.2008.12.012","article-title":"Data-driven Soft Sensors in the process industry","volume":"33","author":"Kadlec","year":"2009","journal-title":"Comput. Chem. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3543","DOI":"10.1021\/ie302069q","article-title":"Review of Recent Research on Data-Based Process Monitoring","volume":"52","author":"Ge","year":"2013","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TIE.2014.2308133","article-title":"Data-Based Techniques Focused on Modern Industry: An Overview","volume":"62","author":"Yin","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.chemolab.2017.09.021","article-title":"Review on data-driven modeling and monitoring for plant-wide industrial processes","volume":"171","author":"Ge","year":"2017","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.compchemeng.2019.04.003","article-title":"Advances and opportunities in machine learning for process data analytics","volume":"126","author":"Qin","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jprocont.2020.03.012","article-title":"Rebooting data-driven soft-sensors in process industries: A review of kernel methods","volume":"89","author":"Liu","year":"2020","journal-title":"J. Process Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17977","DOI":"10.1021\/acs.iecr.0c01942","article-title":"Novel Virtual Sample Generation Based on Locally Linear Embedding for Optimizing the Small Sample Problem: Case of Soft Sensor Applications","volume":"59","author":"Zhu","year":"2020","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, Y.-L., Hua, Q., Zhu, Q.-X., and Lu, S. (2021). Enhanced virtual sample generation based on manifold features: Applications to developing soft sensor using small data. ISA Trans., in press.","DOI":"10.1016\/j.isatra.2021.07.033"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103813","DOI":"10.1016\/j.chemolab.2019.103813","article-title":"Domain adaptation transfer learning soft sensor for product quality prediction","volume":"192","author":"Liu","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"16330","DOI":"10.1021\/acs.iecr.0c02398","article-title":"Development of Adversarial Transfer Learning Soft Sensor for Multi-Grade Processes","volume":"59","author":"Liu","year":"2020","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104903","DOI":"10.1016\/j.conengprac.2021.104903","article-title":"Synthesizing labeled data to enhance soft sensor performance in data-scarce regions","volume":"115","author":"Lyu","year":"2021","journal-title":"Control Eng. Pract."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1016\/j.jprocont.2014.06.015","article-title":"Active learning strategy for smart soft sensor development under a small number of labeled data samples","volume":"24","author":"Ge","year":"2014","journal-title":"J. Process Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.chemolab.2017.11.001","article-title":"A new active learning strategy for soft sensor modeling based on feature reconstruction and uncertainty evaluation","volume":"172","author":"Tang","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_16","first-page":"1","article-title":"Introduction to semi-supervised learning","volume":"3","author":"Zhu","year":"2009","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.3233\/JIFS-169689","article-title":"Semi-supervised regression: A recent review","volume":"35","author":"Kostopoulos","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100150","DOI":"10.1016\/j.ifacsc.2021.100150","article-title":"Semi-supervised data modeling and analytics in the process industry: Current research status and challenges","volume":"16","author":"Ge","year":"2021","journal-title":"IFAC J. Syst. Control"},{"key":"ref_19","first-page":"373","article-title":"A survey on semi-supervised learning","volume":"109","author":"Hoos","year":"2019","journal-title":"Mach. Learn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1109\/TNN.2009.2015974","article-title":"Semi-supervised learning (chapelle, o. et al., eds.; 2006) [book reviews]","volume":"20","author":"Chapelle","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1002\/aic.12422","article-title":"Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples","volume":"57","author":"Ge","year":"2011","journal-title":"AIChE J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jprocont.2018.01.008","article-title":"Semisupervised learning for probabilistic partial least squares regression model and soft sensor application","volume":"64","author":"Zheng","year":"2018","journal-title":"J. Process Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.compchemeng.2017.03.015","article-title":"Mixture semisupervised probabilistic principal component regression model with missing inputs","volume":"103","author":"Sedghi","year":"2017","journal-title":"Comput. Chem. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1109\/TCST.2018.2856845","article-title":"Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression","volume":"27","author":"Shao","year":"2019","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ces.2018.09.031","article-title":"Quality variable prediction for chemical processes based on semisupervised Dirichlet process mixture of Gaussians","volume":"193","author":"Shao","year":"2019","journal-title":"Chem. Eng. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.conengprac.2018.11.008","article-title":"Semi-supervised mixture of latent factor analysis models with application to online key variable estimation","volume":"84","author":"Shao","year":"2019","journal-title":"Control Eng. Pract."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.jprocont.2021.07.013","article-title":"Nonlinear variational Bayesian Student\u2019s-t mixture regression and inferential sensor application with semisupervised data","volume":"105","author":"Wang","year":"2021","journal-title":"J. Process Control"},{"key":"ref_28","unstructured":"Zhu, X. (2002). Learning from Labeled and Unlabeled Data with Label Propagation, Carnegie Mellon University. Tech Report."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.chemolab.2017.10.009","article-title":"Industrial Mooney viscosity prediction using fast semi-supervised empirical model","volume":"171","author":"Zheng","year":"2017","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.chemolab.2018.07.002","article-title":"Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers","volume":"180","author":"Zheng","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7394","DOI":"10.1021\/acs.iecr.5b04118","article-title":"A Framework and Modeling Method of Data-Driven Soft Sensors Based on Semisupervised Gaussian Regression","volume":"55","author":"Yan","year":"2016","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1109\/TIE.2017.2733448","article-title":"Deep Learning of Semisupervised Process Data with Hierarchical Extreme Learning Machine and Soft Sensor Application","volume":"65","author":"Yao","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4589","DOI":"10.1021\/acs.iecr.9b05087","article-title":"Soft Sensor Modeling Method Based on Semisupervised Deep Learning and Its Application to Wastewater Treatment Plant","volume":"59","author":"Yan","year":"2020","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_34","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT press."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.jprocont.2014.01.012","article-title":"Data-driven soft sensor development based on deep learning technique","volume":"24","author":"Shang","year":"2014","journal-title":"J. Process Control"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.ins.2019.11.039","article-title":"Deep relevant representation learning for soft sensing","volume":"514","author":"Yan","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"115509","DOI":"10.1016\/j.ces.2020.115509","article-title":"A novel semi-supervised pre-training strategy for deep networks and its application for quality variable prediction in industrial processes","volume":"217","author":"Yuan","year":"2020","journal-title":"Chem. Eng. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.neucom.2018.11.107","article-title":"Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE","volume":"396","author":"Yuan","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.neucom.2021.07.086","article-title":"Ensemble Deep Relevant Learning Framework for Semi-Supervised Soft Sensor Modeling of Industrial Processes","volume":"462","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s10115-013-0706-y","article-title":"Self-labeled techniques for semi-supervised learning: Taxonomy, software and empirical study","volume":"42","author":"Triguero","year":"2015","journal-title":"Knowl. Inf. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rosenberg, C., Hebert, M., and Schneiderman, H. (2005, January 5\u20137). Semi-Supervised Self-Training of Object Detection Models. Proceedings of the 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV\/MOTION\u201905), Washington, DC, USA.","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Blum, A., and Mitchell, T. (1998, January 24\u201326). Combining labeled and unlabeled data with co-training. Proceedings of the Eleventh Annual Conference on Computational Learning Theory\u2014COLT\u2019 98, Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/279943.279962"},{"key":"ref_43","unstructured":"Zhou, Z.-H., and Li, M. (2005). Semi-supervised regression with co-training. IJCAI, Morgan Kaufmann."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TKDE.2005.186","article-title":"Tri-training: Exploiting unlabeled data using three classifiers","volume":"17","author":"Zhou","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.neucom.2017.03.063","article-title":"Multi-train: A semi-supervised heterogeneous ensemble classifier","volume":"249","author":"Gu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TSMCA.2007.904745","article-title":"Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples","volume":"37","author":"Li","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.eswa.2015.12.027","article-title":"Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing","volume":"51","author":"Kang","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.chemolab.2015.08.002","article-title":"Co-training partial least squares model for semi-supervised soft sensor development","volume":"147","author":"Bao","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"103970","DOI":"10.1016\/j.chemolab.2020.103970","article-title":"Development of semi-supervised multiple-output soft-sensors with Co-training and tri-training MPLS and MRVM","volume":"199","author":"Li","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ins.2019.01.038","article-title":"Air quality estimation by exploiting terrain features and multi-view transfer semi-supervised regression","volume":"483","author":"Lv","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"100052","DOI":"10.1016\/j.egyai.2021.100052","article-title":"A co-training style semi-supervised artificial neural network modeling and its application in thermal conductivity prediction of polymeric composites filled with BN sheets","volume":"4","author":"Liang","year":"2021","journal-title":"Energy AI"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.chemolab.2014.06.008","article-title":"Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants","volume":"137","author":"Kaneko","year":"2014","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7320","DOI":"10.1021\/acs.iecr.5b01495","article-title":"Adaptive Soft Sensor Development Based on Online Ensemble Gaussian Process Regression for Nonlinear Time-Varying Batch Processes","volume":"54","author":"Jin","year":"2015","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"107060","DOI":"10.1016\/j.asoc.2020.107060","article-title":"Adaptive ranking based ensemble learning of Gaussian process regression models for quality-related variable prediction in process industries","volume":"101","author":"Liu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105806","DOI":"10.1016\/j.asoc.2019.105806","article-title":"Ensemble regularized local finite impulse response models and soft sensor application in nonlinear dynamic industrial processes","volume":"85","author":"Chen","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms, Taylor & Francis.","DOI":"10.1201\/b12207"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.patrec.2019.07.022","article-title":"A multi-scheme semi-supervised regression approach","volume":"125","author":"Fazakis","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s10618-011-0243-9","article-title":"Exploiting unlabeled data to enhance ensemble diversity","volume":"26","author":"Zhang","year":"2011","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TII.2020.2969709","article-title":"Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data","volume":"17","author":"Sun","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.neucom.2016.10.005","article-title":"Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development","volume":"222","author":"Shao","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1016\/S0893-6080(99)00073-8","article-title":"Ensemble learning via negative correlation","volume":"12","author":"Liu","year":"1999","journal-title":"Neural Netw."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cherd.2015.06.009","article-title":"Data driven soft sensor development for complex chemical processes using extreme learning machine","volume":"102","author":"He","year":"2015","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"17991","DOI":"10.1021\/acs.iecr.9b03702","article-title":"Soft Sensor Development for Nonlinear Industrial Processes Based on Ensemble Just-in-Time Extreme Learning Machine through Triple-Modal Perturbation and Evolutionary Multiobjective Optimization","volume":"58","author":"Pan","year":"2019","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.chemolab.2018.12.002","article-title":"Ensemble just-in-time learning framework through evolutionary multi-objective optimization for soft sensor development of nonlinear industrial processes","volume":"184","author":"Jin","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1109\/TII.2021.3065377","article-title":"Novel L1 Regularized Extreme Learning Machine for Soft-Sensing of an Industrial Process","volume":"18","author":"Shi","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_67","first-page":"1621","article-title":"Managing diversity in regression ensembles","volume":"6","author":"Brown","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ins.2013.12.016","article-title":"Fast decorrelated neural network ensembles with random weights","volume":"264","author":"Alhamdoosh","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5366","DOI":"10.1109\/TNNLS.2017.2784814","article-title":"Semisupervised Negative Correlation Learning","volume":"29","author":"Chen","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"116560","DOI":"10.1016\/j.ces.2021.116560","article-title":"Evolutionary optimization based pseudo labeling for semi-supervised soft sensor development of industrial processes","volume":"237","author":"Jin","year":"2021","journal-title":"Chem. Eng. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","article-title":"Ensembling neural networks: Many could be better than all","volume":"137","author":"Zhou","year":"2002","journal-title":"Artif. Intell."},{"key":"ref_72","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2008). Gaussian Processes for Machine Learning, Print, MIT Press."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Bansal, J.C., Singh, P.K., and Pal, N.R. (2019). Evolutionary and Swarm Intelligence Algorithms, Springer.","DOI":"10.1007\/978-3-319-91341-4"},{"key":"ref_74","unstructured":"Dasgupta, D., and Michalewicz, Z. (2013). Evolutionary Algorithms in Engineering Applications, Springer Science & Business Media."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Huang, L., Deng, X., Bo, Y., Zhang, Y., and Wang, P. (2021). Evolutionary optimization assisted delayed deep cycle reservoir modeling method with its application to ship heave motion prediction. ISA Trans., in press.","DOI":"10.1016\/j.isatra.2021.08.020"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isatra.2020.09.017","article-title":"Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery","volume":"113","author":"Ma","year":"2021","journal-title":"ISA Trans."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.isatra.2020.05.041","article-title":"Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO","volume":"105","author":"Haidong","year":"2020","journal-title":"ISA Trans."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s13042-019-01030-4","article-title":"Large-scale evolutionary optimization: A survey and experimental comparative study","volume":"11","author":"Jian","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.cherd.2015.01.006","article-title":"Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models","volume":"95","author":"Shao","year":"2015","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2021.04.028","article-title":"Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models","volume":"174","author":"Jin","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s11704-019-8452-2","article-title":"Safe semi-supervised learning: A brief introduction","volume":"13","author":"Li","year":"2019","journal-title":"Front. Comput. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"13227","DOI":"10.1021\/ie3020186","article-title":"Multiway Gaussian Mixture Model Based Adaptive Kernel Partial Least Squares Regression Method for Soft Sensor Estimation and Reliable Quality Prediction of Nonlinear Multiphase Batch Processes","volume":"51","author":"Yu","year":"2012","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.ces.2015.03.038","article-title":"Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes","volume":"131","author":"Jin","year":"2015","journal-title":"Chem. Eng. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1016\/S0098-1354(02)00127-8","article-title":"A modular simulation package for fed-batch fermentation: Penicillin production","volume":"26","author":"Birol","year":"2002","journal-title":"Comput. Chem. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8471\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:51:50Z","timestamp":1760169110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,19]]},"references-count":84,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21248471"],"URL":"https:\/\/doi.org\/10.3390\/s21248471","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,12,19]]}}}