Finding Originally Mislabels with MD-ELM

Anton Akusok, David Veganzones, Yoan Miche, Eric Séverin, Amaury Lendasse

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

6 Citations (Scopus)


This paper presents a method which aims at detecting misla-beled samples, with a practical example in the field of bankruptcy predic-tion. Mislabeled samples are found in many classification problems and can bias the training of the desired classifier. This paper is proposing a new method based on Extreme Learning Machine (ELM) which allows for identification of the most probable mislabeled samples. Two datasets are used in order to validate and test the proposed methodology: a toy exam-ple (XOR problem) and a real dataset from corporate finance (bankruptcy prediction).
Original languageEnglish
Title of host publicationESANN 2014 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence
Publication date2014
Publication statusPublished - 2014
MoE publication typeA4 Article in conference proceedings


  • 512 Business and Management


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