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Data sanitization against label flipping attacks using AdaBoost-based semi-supervised learning technology
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DOI：10.1007/s00500-021-06384-y.png)
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Abstract
En 中文
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 similar to 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.
Keywords:
Label noise detection
Machine learning
AdaBoost algorithm
Semi-supervised
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