Distance Estimation for Incomplete Data by Extreme Learning Machine

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

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
OriginalspråkEngelska
Titel på gästpublikationInternational Conference on Extreme Learning Machine: Proceedings of ELM-2017
Antal sidor7
UtgivningsortCham
FörlagSpringer
Utgivningsdatum17.10.2018
Sidor203-209
ISBN (tryckt)978-3-030-01519-0
ISBN (elektroniskt)978-3-030-01520-6
DOI
StatusPublicerad - 17.10.2018
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang2017 the 8th International Conference on Extreme Learning Machines (ELM) - Yantai, Kina
Varaktighet: 04.10.201707.10.2017
http://www.ntu.edu.sg/home/egbhuang/elm2017/index.html

Publikationsserier

NamnProceedings in Adaptation, Learning and Optimization (PALO)
Volym 10

Nyckelord

  • 512 Företagsekonomi

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