{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T13:59:20Z","timestamp":1785160760893,"version":"3.55.0"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100019687","name":"Hamad Bin Khalifa University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019687","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Chemical Engineering"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.compchemeng.2026.109818","type":"journal-article","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T06:46:40Z","timestamp":1784962000000},"page":"109818","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A comparative study of multiclass fault classification in industrial processes: Conventional, binary decomposition, and residual-based classifiers with interval-valued features"],"prefix":"10.1016","volume":"214","author":[{"given":"Nour","family":"Basha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byanne","family":"Malluhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hazem","family":"Nounou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0520-9623","authenticated-orcid":false,"given":"Mohamed","family":"Nounou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.compchemeng.2026.109818_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2018.e00938","article-title":"State-of-the-art in artificial neural network applications: A survey","volume":"4","author":"Abiodun","year":"2018","journal-title":"Heliyon"},{"key":"10.1016\/j.compchemeng.2026.109818_b2","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1214\/09-SS054","article-title":"A survey of cross-validation procedures for model selection","volume":"4","author":"Arlot","year":"2010","journal-title":"Stat. Surv."},{"key":"10.1016\/j.compchemeng.2026.109818_b3","series-title":"Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers","first-page":"39","article-title":"Support vector machines for classification","author":"Awad","year":"2015"},{"key":"10.1016\/j.compchemeng.2026.109818_b4","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10100-017-0479-6","article-title":"A framework for sensitivity analysis of decision trees","volume":"26","author":"B. Kami\u0144ski","year":"2018","journal-title":"Cent. Eur. J. Oper. Res."},{"key":"10.1016\/j.compchemeng.2026.109818_b5","series-title":"Advanced Data-Driven Process Monitoring Framework and its Application to Chemical Processes","author":"Basha","year":"2024"},{"key":"10.1016\/j.compchemeng.2026.109818_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.jgsce.2023.204964","article-title":"Bayesian-optimized neural networks and their application to model gas-to-liquid plants","volume":"113","author":"Basha","year":"2023","journal-title":"Gas Sci. Eng."},{"key":"10.1016\/j.compchemeng.2026.109818_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2022.108126","article-title":"Bayesian-optimized gaussian process-based fault classification in industrial processes","volume":"170","author":"Basha","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109818_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2018.04.017","article-title":"Multivariate fault detection and classification using interval principal component analysis","volume":"27","author":"Basha","year":"2018","journal-title":"J. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109818_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.106786","article-title":"Multiclass data classification using fault detection-based techniques","volume":"136","author":"Basha","year":"2020","journal-title":"Comput. Chem. Eng."},{"issue":"8","key":"10.1016\/j.compchemeng.2026.109818_b10","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.ifacol.2015.08.199","article-title":"Revision of the tennessee eastman process model","volume":"48","author":"Bathelt","year":"2015","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.compchemeng.2026.109818_b11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compchemeng.2026.109818_b12","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: Applications, challenges and trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109818_b13","first-page":"215","article-title":"An improved random forest classifier for multi-class classification","volume":"3","author":"Chaudhary","year":"2016","journal-title":"Inf. Process. Agric."},{"issue":"2","key":"10.1016\/j.compchemeng.2026.109818_b14","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/S0169-7439(99)00061-1","article-title":"Fault diagnosis in chemical processes using fisher discriminant analysis, discriminant partial least squares, and principal component analysis","volume":"50","author":"Chiang","year":"2000","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"10.1016\/j.compchemeng.2026.109818_b15","series-title":"Fault Detection and Diagnosis in Industrial Systems","author":"Chiang","year":"2001"},{"key":"10.1016\/j.compchemeng.2026.109818_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2021.112408","article-title":"Application of principal component analysis (pca) to the assessment of parameter correlations in the partial-nitrification process using aerobic granular sludge","volume":"288","author":"Cui","year":"2021","journal-title":"J. Environ. Manag."},{"key":"10.1016\/j.compchemeng.2026.109818_b17","series-title":"Variable frequency sine wave - file exchange - matlab central","author":"Danziger","year":"2013"},{"key":"10.1016\/j.compchemeng.2026.109818_b18","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3633702","article-title":"Improved machine learning for multiclass fault classification in industrial processes","author":"Dhibi","year":"2025","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109818_b19","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0098-1354(93)80018-I","article-title":"A plant-wide industrial process control problem","volume":"17","author":"Downs","year":"1993","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109818_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.129221","article-title":"Bayesian optimization of multiscale kernel principal component analysis and its application to model gas-to-liquid (gtl) process data","volume":"284","author":"Fezai","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.compchemeng.2026.109818_b21","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1016\/j.neucom.2015.10.018","article-title":"An improved svm integrated gs-pca fault diagnosis approach of tennessee eastman process","volume":"174","author":"Gao","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2026.109818_b22","series-title":"Bayesian optimization","first-page":"1","author":"Garnett","year":"2015"},{"key":"10.1016\/j.compchemeng.2026.109818_b23","series-title":"Bayesian optimization with unknown constraints","first-page":"1","author":"Gelbart","year":"2014"},{"key":"10.1016\/j.compchemeng.2026.109818_b24","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.eswa.2018.08.021","article-title":"A generalized mean distance-based k-nearest neighbor classifier","volume":"115","author":"Gou","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compchemeng.2026.109818_b25","series-title":"An Introduction to Neural Networks","author":"Gurney","year":"2018"},{"key":"10.1016\/j.compchemeng.2026.109818_b26","doi-asserted-by":"crossref","unstructured":"Heo, S., Lee, J.H., 2018. Fault detection and classification using artificial neural networks. In: 10th IFAC Symposium on Advanced Control of Chemical Processes. Vol. 51, pp. 470\u2013475, (18).","DOI":"10.1016\/j.ifacol.2018.09.380"},{"key":"10.1016\/j.compchemeng.2026.109818_b27","doi-asserted-by":"crossref","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A"},{"key":"10.1016\/j.compchemeng.2026.109818_b28","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.neucom.2014.08.006","article-title":"Constructing a multi-class classifier using one-against-one approach with different binary classifiers","volume":"149","author":"Kang","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2026.109818_b29","series-title":"Support Vector Machines: Theory and Applications","first-page":"1","article-title":"Support vector machines \u2013 an introduction","author":"Kecman","year":"2005"},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109818_b30","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10661-022-10555-1","article-title":"Assessment of groundwater salinity using principal component analysis (pca): a case study from mewat (nuh), haryana, india","volume":"195","author":"Krishan","year":"2023","journal-title":"Environ. Monit. Assess."},{"issue":"10","key":"10.1016\/j.compchemeng.2026.109818_b31","doi-asserted-by":"crossref","first-page":"2581","DOI":"10.1002\/aic.11576","article-title":"Diagnosis of process faults in chemical systems using a local partial least squares approach","volume":"54","author":"Kruger","year":"2008","journal-title":"AIChE J."},{"key":"10.1016\/j.compchemeng.2026.109818_b32","series-title":"Computer Vision: A Reference Guide","first-page":"1","article-title":"Principal component analysis (pca)","author":"Kurita","year":"2019"},{"key":"10.1016\/j.compchemeng.2026.109818_b33","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1080\/10618600.2012.679895","article-title":"Symbolic covariance principal component analysis and visualization for interval-valued data","volume":"2","author":"Le-Rademacher","year":"2012","journal-title":"J. Comput. Graph. Statist."},{"key":"10.1016\/j.compchemeng.2026.109818_b34","article-title":"Fault detection in tennessee eastman process with temporal deep learning models","volume":"23","author":"Lomov","year":"2021","journal-title":"J. Ind. Inf. Integr."},{"key":"10.1016\/j.compchemeng.2026.109818_b35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/B978-0-12-409545-8.00002-9","article-title":"Feature selection and extraction","volume":"2","author":"Meyer-Baese","year":"2014","journal-title":"Pattern Recognit. Signal Anal. Med. Imaging"},{"issue":"15","key":"10.1016\/j.compchemeng.2026.109818_b36","doi-asserted-by":"crossref","first-page":"3301","DOI":"10.1093\/bioinformatics\/bti499","article-title":"Prediction error estimation: a comparison of resampling methods","volume":"21","author":"Molinaro","year":"2005","journal-title":"Bioinformatics"},{"key":"10.1016\/j.compchemeng.2026.109818_b37","series-title":"Advances in Data-Driven Modeling, Fault Detection, and Fault Identification: Applications to Chemical Processes","author":"Nounou","year":"2026"},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109818_b38","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1002\/aic.16497","article-title":"A non-linear support vector machine-based feature selection approach for fault detection and diagnosis: Application to the tennessee eastman process","volume":"65","author":"Onel","year":"2019","journal-title":"AIChE J."},{"key":"10.1016\/j.compchemeng.2026.109818_b39","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.compchemeng.2026.109818_b40","doi-asserted-by":"crossref","unstructured":"Parmar, A., Katariya, R., Patel, V., 2018. A review on random forest: An ensemble classifier. In: International Conference on Intelligent Data Communication Technologies and Internet of Things. (ICICI), pp. 758\u2013763.","DOI":"10.1007\/978-3-030-03146-6_86"},{"key":"10.1016\/j.compchemeng.2026.109818_b41","series-title":"Additional Tennessee eastman process simulation data for anomaly detection evaluation","author":"Rieth","year":"2017"},{"key":"10.1016\/j.compchemeng.2026.109818_b42","first-page":"101","article-title":"In defense of one-vs-all classification","volume":"5","author":"Rifkin","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compchemeng.2026.109818_b43","series-title":"Tennessee Eastman Process","first-page":"99","author":"Russell","year":"2000"},{"key":"10.1016\/j.compchemeng.2026.109818_b44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmp.2018.03.001","article-title":"A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions","volume":"85","author":"Schulz","year":"2018","journal-title":"J. Math. Psych."},{"key":"10.1016\/j.compchemeng.2026.109818_b45","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.compag.2016.10.006","article-title":"Behavior classification of cows fitted with motion collars: Decomposing multi-class classification into a set of binary problems","volume":"131","author":"Smith","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compchemeng.2026.109818_b46","series-title":"Practical bayesian optimization of machine learning algorithms","first-page":"1","author":"Snoek","year":"2012"},{"issue":"2","key":"10.1016\/j.compchemeng.2026.109818_b47","first-page":"130","article-title":"Decision tree methods: applications for classification and prediction","volume":"27","author":"Song","year":"2015","journal-title":"Shanghai Arch. Psychiatry"},{"key":"10.1016\/j.compchemeng.2026.109818_b48","series-title":"IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No. 01CH 37188)","first-page":"287","article-title":"Non-linear modelling and support vector machines","volume":"Vol. 1","author":"Suykens","year":"2001"},{"key":"10.1016\/j.compchemeng.2026.109818_b49","series-title":"Deep learning using linear support vector machines","author":"Tang","year":"2013"},{"key":"10.1016\/j.compchemeng.2026.109818_b50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00349-y","article-title":"Boosting methods for multi-class imbalanced data classification: an experimental review","volume":"7","author":"Tanha","year":"2020","journal-title":"J. Big Data"},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109818_b51","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0098-1354(02)00162-X","article-title":"A review of process fault detection and diagnosis: Part iii: Process history based methods","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"issue":"4","key":"10.1016\/j.compchemeng.2026.109818_b52","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1198\/004017005000000256","article-title":"The inertial properties of quality control charts","volume":"47","author":"Woodall","year":"2005","journal-title":"Technometrics"},{"key":"10.1016\/j.compchemeng.2026.109818_b53","series-title":"2018 International Conference on Computational Science and Computational Intelligence","first-page":"301","article-title":"An implementation of naive bayes classifier","author":"Yang","year":"2018"},{"issue":"5","key":"10.1016\/j.compchemeng.2026.109818_b54","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.ces.2008.10.012","article-title":"Enhanced statistical analysis of non-linear processes using kpca, kica and svm","volume":"64","author":"Zhang","year":"2009","journal-title":"Chem. Eng. Sci."},{"issue":"11","key":"10.1016\/j.compchemeng.2026.109818_b55","doi-asserted-by":"crossref","DOI":"10.21037\/atm.2016.03.37","article-title":"Introduction to machine learning: k-nearest neighbors","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann. Transl. Med."},{"issue":"12","key":"10.1016\/j.compchemeng.2026.109818_b56","doi-asserted-by":"crossref","DOI":"10.21037\/atm.2016.03.38","article-title":"Na\u00efve bayes classification in r","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann. Transl. Med."}],"container-title":["Computers &amp; Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S009813542600270X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S009813542600270X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T13:03:19Z","timestamp":1785157399000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S009813542600270X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":56,"alternative-id":["S009813542600270X"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109818","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A comparative study of multiclass fault classification in industrial processes: Conventional, binary decomposition, and residual-based classifiers with interval-valued features","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109818","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109818"}}