Reinforced extreme learning machines for fast robust regression in the presence of outliers

Benoît Frenay, Michel Verleysen

Research output: Contribution to journalArticle

Abstract

Extreme learning machines (ELMs) are fast methods that obtain state-of-the-art results in regression. However, they are not robust to outliers and their meta-parameter (i.e., the number of neurons for standard ELMs and the regularization constant of output weights for L2-regularized ELMs) selection is biased by such instances. This paper proposes a new robust inference algorithm for ELMs which is based on the pointwise probability reinforcement methodology. Experiments show that the proposed approach produces results which are comparable to the state of the art, while being often faster.

Original languageEnglish
Article number7358117
Pages (from-to) 3351-3363
JournalIEEE Transactions on Cybernetics
VolumePP
Issue number99
DOIs
Publication statusPublished - 17 Dec 2015

Keywords

  • Extreme learning machines (ELMs)
  • Outliers
  • Pointwise probability reinforcements (PPRs)
  • Regularization
  • Robust inference

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