Null-Labelling: A Generic Approach for Learning in the Presence of Class Noise
| Autor: | M. Asif Naeem, Russel Pears, Benjamin Denham |
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| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
Noise measurement Computer science business.industry 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Class (biology) Noise ComputingMethodologies_PATTERNRECOGNITION Null (SQL) Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences computer.programming_language Interpretability |
| Zdroj: | ICDM |
| DOI: | 10.1109/icdm50108.2020.00114 |
| Popis: | Datasets containing class noise present significant challenges to accurate classification, thus requiring classifiers that can refuse to classify noisy instances. We demonstrate the inability of the popular confidence-thresholding rejection method to learn from relationships between input features and not-at-random class noise. To take advantage of these relationships, we propose a novel null-labelling scheme based on iterative re-training with relabelled datasets that uses a classifier to learn to reject instances that are likely to be misclassified. We demonstrate the ability of null-labelling to achieve a significantly better tradeoff between classification error and coverage than the confidence-thresholding method. Models generated by the null-labelling scheme have the added advantage of interpretability, in that they are able to identify features correlated with class noise. We also unify prior theories for combining and evaluating sets of rejecting classifiers. |
| Databáze: | OpenAIRE |
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