Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning

Anton Akusok*, Leonardo Espinosa Leal, Kaj-Mikael Björk, Amaury Lendasse

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM (, Usability and flexibility of the approach are the main focus points in this work, achieved primarily through a tight integration with Scikit-Learn, a de facto industry standard library in Machine Learning outside Deep Learning. Methodological advances enable great flexibility in dynamic addition of new classes to a trained model, or by allowing a model to forget previously learned data.
Original languageEnglish
Title of host publicationProceedings of ELM2019
EditorsJiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Place of PublicationCham
Publication date2021
ISBN (Print)978-3-030-58988-2
ISBN (Electronic)978-3-030-58989-9
Publication statusPublished - 2021
MoE publication typeA4 Article in conference proceedings

Publication series

NameProceedings in Adaptation, Learning and Optimization
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092


  • 113 Computer and information sciences


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