1. MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model.
- Author
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Akusok, Anton, Veganzones, David, Miche, Yoan, Björk, Kaj-Mikael, Jardin, Philippe du, Severin, Eric, and Lendasse, Amaury
- Subjects
- *
MACHINE learning , *MACHINE theory , *INFORMATION science , *COMPUTER science , *ESTIMATION theory - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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