1. MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model
- Author
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David Veganzones, Kaj-Mikael Björk, Philippe du Jardin, Yoan Miche, Amaury Lendasse, Anton Akusok, and Eric Séverin
- Subjects
ta113 ,Extreme learning machine ,Computer science ,business.industry ,Cognitive Neuroscience ,Sample (statistics) ,computer.software_genre ,Machine learning ,Generalization error ,Class (biology) ,Computer Science Applications ,Artificial Intelligence ,Data mining ,Artificial intelligence ,business ,computer - 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.
- Published
- 2015