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.
|Peer-reviewed scientific journal||Neurocomputing|
|Number of pages||9|
|Publication status||Published - 14.02.2015|
|MoE publication type||A1 Journal article - refereed|
- 512 Business and Management
- Extreme Learning Machine