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dc.contributor.authorMarchi, Lorenzo
dc.contributor.authorKrylov, Ivan
dc.contributor.authorRoginski, Robert T.
dc.contributor.authorWise, Barry and Di Donato, Francesca
dc.contributor.authorNieto-Ortega, Sonia
dc.contributor.authorPereira, Jose Francielson Q.
dc.contributor.authorBro, Rasmus
dc.date.accessioned2023-10-04T10:45:07Z-
dc.date.available2023-10-04T10:45:07Z-
dc.date.issued2022
dc.identifierWOS:000888912600001
dc.identifier.issn0886-9383
dc.identifier.urihttp://dspace.azti.es/handle/24689/1616-
dc.description.abstractWhen building classification models of complex systems with many classes, the traditional chemometric approaches such as discriminant analysis or soft independent modeling of class analogy often fail. Some people resort to advanced deep neural network, but this is only an option if there is access to very many samples. Another alternative often used is to build hierarchical models where subclasses are sort of peeled off one or a few at a time. Such approaches often outperform classical classification as well as deep neural network on small multi-class problems. The downside though is that it is very cumbersome to build such hierarchies of models. It requires substantial work of a skilled person. In this paper, we develop a fully automated approach for building hierarchical models and test the performance on a number of classification problems.
dc.language.isoEnglish
dc.publisherWILEY
dc.subjectautomation
dc.subjectclassification
dc.subjecthierarchical
dc.subjectCLASSIFICATION MODELS
dc.subjectDISCRIMINATION
dc.titleAutomatic hierarchical model builder
dc.typeArticle
dc.identifier.journalJOURNAL OF CHEMOMETRICS
dc.format.volume36
dc.contributor.funderRussian Foundation for Basic Research [20-33-90280]
dc.identifier.e-issn1099-128X
dc.identifier.doi10.1002/cem.3455
Appears in Publication types:Artículos científicos



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