Extreme Learning Tree

Anton Akusok, Emil Eirola, Kaj-Mikael Björk, Amaury Lendasse

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

Abstract

The paper proposes a new variant of a decision tree, called an Extreme Learning Tree. It consists of an extremely random tree with non-linear data transformation, and a linear observer that provides predictions based on the leaf index where the data samples fall. The proposed method outperforms linear models on a benchmark dataset, and may be a building block for a future variant of Random Forest.
Original languageEnglish
Title of host publicationInternational Conference on Extreme Learning Machine: Proceedings of ELM-2017
Number of pages5
Place of PublicationCham
PublisherSpringer
Publication date17.10.2018
Pages181-185
ISBN (Print)978-3-030-01519-0
ISBN (Electronic)978-3-030-01520-6
DOIs
Publication statusPublished - 17.10.2018
MoE publication typeA4 Article in conference proceedings
Event2017 the 8th International Conference on Extreme Learning Machines (ELM) - Yantai, China
Duration: 04.10.201707.10.2017
http://www.ntu.edu.sg/home/egbhuang/elm2017/index.html

Publication series

NameProceedings in Adaptation, Learning and Optimization (PALO)
Volume10

Keywords

  • 512 Business and Management
  • ELM
  • Decision tree
  • Randomized methods

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  • Cite this

    Akusok, A., Eirola, E., Björk, K-M., & Lendasse, A. (2018). Extreme Learning Tree. In International Conference on Extreme Learning Machine: Proceedings of ELM-2017 (pp. 181-185 ). (Proceedings in Adaptation, Learning and Optimization (PALO); Vol. 10). Springer. https://doi.org/10.1007/978-3-030-01520-6_16