Abstract
The label flipping attack is a special poisoning attack in the adversarial environment. The research designed a novel label noise processing framework, the core of which is the semi-supervised learning label correction algorithm based on AdaBoost (AdaSSL). It can effectively improve the label quality of training data and improve the classification performance of the model. Based on five real UCI datasets, this study chose six classic machine learning algorithms (NB, LR, SVM, DT, KNN and MLP) as the base classifiers to classify them. With a noise level of 0\( \sim \)20%, we evaluated the classification effect of these classifiers on UCI datasets based on the entropy label flipping attack and the AdaSSL defense algorithm. The experimental results show that the AdaSSL algorithm can effectively improve the robustness of the classifier against label flipping attack. Compared with the most advanced semi-supervised defense algorithm in the literature, the algorithm does not need to use additional datasets. At a noise ratio of 10%, the AdaSSL algorithm is significantly better than state-of-the-art label noise defense technology.
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Acknowledgements
This research was partly supported by the Key R&D and promotion projects of Henan Province (Technological research) (Grant No. 212102210143), the Key Science and Technology Project of Xinjiang Production and Construction Corps (Grant No. 2018AB017) and the Key Research, Development, and Dissemination Program of Henan Province (Science and Technology for the People) (Grant No. 182207310002).
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Hongpo Zhang contributed to the conception of the study; Ning Cheng performed the experiment; Hongpo Zhang contributed significantly to analysis and manuscript preparation; Ning Cheng performed the data analyses and wrote the manuscript; Zhanbo Li helped perform the analysis with constructive discussions.
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Cheng, N., Zhang, H. & Li, Z. Data sanitization against label flipping attacks using AdaBoost-based semi-supervised learning technology. Soft Comput 25, 14573–14581 (2021). https://doi.org/10.1007/s00500-021-06384-y
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DOI: https://doi.org/10.1007/s00500-021-06384-y
Keywords
- Label noise detection
- Machine learning
- AdaBoost algorithm
- Semi-supervised