关键词: 噪声标记, 直推式支持向量机, 聚类算法, 鲁棒性

Abstract: With the rapid development of deep learning, a large amount of labeled data is required. But the original data often has an unknown proportion of noise labels, which will directly affect the final result of the classifier. To deal with the problem of the existence of error labels in datasets, this paper proposes an improved TSVM algorithm adapted to noise labels data. This method uses clustering to filter clusters with higher error rate, and then exchanges the two clusters with higher error rate to reduce the transfer and accumulation of noise labels in the TSVM algorithm. The method can improve the accuracy effectively and enhance the robustness of the TSVM classifier in the data set with different proportions of noise. In order to verify the effectiveness of the proposed algorithm, experiments are performed by adding different proportions of noise tags to the selected UCI data set. Experimental results show that the robustness of proposed algorithm is better than SVM and TSVM in the datasets with different noise ratios.

Key words: noisy label, transductive support vector machines, clustering algorithm, robustness