Evaluating Confidence Intervals for ELM Predictions

Anton Akusok, Yoan Miche, Kaj-Mikael Björk, Rui Nian, Paula Lauren, Amaury Lendasse

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

This paper proposes a way of providing more useful and interpretable results for ELM models by adding confidence intervals to predictions. Unlike a usual statistical approach with Mean Squared Error (MSE) that evaluates an average performance of an ELM model over the whole dataset, the proposed method computed particular confidence intervals for each data sample. A confidence for each particular sample makes ELM predictions more intuitive to interpret, and an ELM model more applicable in practice under task-specific requirements. The method shows good results on both toy and a real skin segmentation datasets. On a toy dataset, the predicted confidence intervals accurately represent a variable magnitude noise. On a real dataset, classification with a confidence interval improves the precision at the cost of recall.
OriginalspråkEngelska
Titel på gästpublikationProceedings of ELM-2015
Antal sidor10
Volym2
UtgivningsortCham
FörlagSpringer
Utgivningsdatum03.01.2016
Sidor413-422
ISBN (tryckt)978-3-319-28372-2
ISBN (elektroniskt)978-3-319-28373-9
DOI
StatusPublicerad - 03.01.2016
MoE-publikationstypA4 Artikel i en konferenspublikation

Publikationsserier

NamnProceedings in Adaptation, Learning and Optimization (PALO)
Volym7

Nyckelord

  • 512 Företagsekonomi

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