Validating Untrained Human Annotations Using Extreme Learning Machines

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

*Motsvarande författare för detta arbete

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKapitelVetenskapligPeer review

Sammanfattning

We present a process for validating and improving annotations made by untrained humans using a two-step machine learning algorithm. The initial validation algorithm is trained on a high quality annotated subset of the data that the untrained humans are asked to annotate. We continue by using the machine learning algorithm to predict other samples that are also annotated by the humans and test several approaches for joining the algorithmic annotations with the human annotations, with the intention of improving the performance beyond using either approach individually. We show that combining human annotations with the algorithmic predictions can improve the accuracy of the annotations.
OriginalspråkEngelska
Titel på gästpublikationProceedings of ELM2019
RedaktörerJiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Antal sidor10
FörlagSpringer
Utgivningsdatum2021
Sidor89-98
ISBN (tryckt)978-3-030-58988-2
ISBN (elektroniskt)978-3-030-58989-9
DOI
StatusPublicerad - 2021
MoE-publikationstypA3 Del av bok eller annat samlingsverk

Publikationsserier

NamnProceedings of ELM2019
Volym14
ISSN (tryckt)2363-6084
ISSN (elektroniskt)2363-6092

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