Naïve Bayes classifier based on reliability measurement for datasets with noisy labels
Author:
Funder
National Social Science Fund of China
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Link
https://link.springer.com/content/pdf/10.1007/s10479-023-05671-1.pdf
Reference38 articles.
1. Ahmad, I. (2019). Performance of classifiers on noisy-labeled training data: An empirical study on handwritten digit classification task. In International work-conference on artificial neural networks.
2. Anderson, B., & McGrew, D. (2017). Machine learning for encrypted malware traffic classification: Accounting for noisy labels and non-stationarity. In ACM SIGKDD international conference on knowledge discovery and data mining.
3. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv:1409.0473
4. Bekker, A.J., & Goldberger, J. (2016). Training deep neural-networks based on unreliable labels. In IEEE international conference on acoustics, speech and signal processing (ICASSP).
5. Brodley, C. E., & Friedl, M. A. (1999). Identifying mislabeled training data. Journal of Artificial Intelligence Research, 11, 131–167.
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