Distance Estimation for Incomplete Data by Extreme Learning Machine

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

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

Abstract

Data with missing values are very common in practice, yet many machine learning models are not designed to handle incomplete data. As most machine learning approaches can be formulated in terms of distance between samples, estimating these distances on data with missing values provides an effective way to use such models. This paper present a procedure to estimate the distances using the Extreme Learning Machine. Experimental comparison shows that the proposed approach achieves competitive accuracy with other methods on standard benchmark datasets.
Original languageEnglish
Title of host publicationInternational Conference on Extreme Learning Machine: Proceedings of ELM-2017
Number of pages7
Place of PublicationCham
PublisherSpringer
Publication date17.10.2018
Pages203-209
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)
Volume 10

Keywords

  • 512 Business and Management
  • Machine learning
  • Extreme learning machine
  • Incomplete data
  • Missing values
  • Distance estimation

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  • Cite this

    Eirola, E., Akusok, A., Björk, K-M., & Lendasse, A. (2018). Distance Estimation for Incomplete Data by Extreme Learning Machine. In International Conference on Extreme Learning Machine: Proceedings of ELM-2017 (pp. 203-209). (Proceedings in Adaptation, Learning and Optimization (PALO); Vol. 10). Cham: Springer. https://doi.org/10.1007/978-3-030-01520-6_18