Automatic hierarchical model builder

Research output: Contribution to journalJournal articleResearchpeer-review


  • Fulltext

    Final published version, 639 KB, PDF document

  • Lorenzo Marchi
  • Ivan Krylov
  • Robert T. Roginski
  • Barry Wise
  • Francesca Di Donato
  • Sonia Nieto-Ortega
  • José Francielson Q. Pereira
  • Bro, Rasmus

When 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.

Original languageEnglish
Article numbere3455
JournalJournal of Chemometrics
Issue number12
Number of pages8
Publication statusPublished - 2022

Bibliographical note

Funding Information:
The contribution of Ivan Krylov to the study was partially funded by Russian Foundation for Basic Research (RFBR), project number 20‐33‐90280, and Danish Government Scholarship under the Cultural Agreements.

Publisher Copyright:
© 2022 The Authors. Journal of Chemometrics published by John Wiley & Sons Ltd.

    Research areas

  • automation, classification, hierarchical

Number of downloads are based on statistics from Google Scholar and

No data available

ID: 327671914