MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model

Anton Akusok, David Veganzones, Yoan Miche, Kaj-Mikael Björk, Philippe du Jardin, Eric Severin, Amaury Lendasse

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)

Abstract

This paper proposes a methodology for identifying data samples that are likely to be mislabeled in a c-class classification problem (dataset). The methodology relies on an assumption that the generalization error of a model learned from the data decreases if a label of some mislabeled sample is changed to its correct class. A general classification model used in the paper is OP-ELM; it also provides a fast way to estimate the generalization error by PRESS Leave-One-Out. It is tested on two toy datasets, as well as on real life datasets for one of which expert knowledge about the identified potential mislabels has been sought.
Original languageEnglish
Peer-reviewed scientific journalNeurocomputing
Volume159
Issue numberJuly
Pages (from-to)242-250
Number of pages9
ISSN0925-2312
DOIs
Publication statusPublished - 14.02.2015
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management
  • Mislabels
  • Extreme Learning Machine
  • Classification

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    Akusok, A., Veganzones, D., Miche, Y., Björk, K-M., du Jardin, P., Severin, E., & Lendasse, A. (2015). MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model. Neurocomputing, 159(July), 242-250. https://doi.org/10.1016/j.neucom.2015.01.055