Abstract
Label noises exist in many applications, which usually add difficulties for data analysis. A straightforward and effective method is to detect and filter out them prior to training. Ensemble learning based filter has shown promising performances. We define an important parameter to improve the performance of the algorithm. The proposed method is cost sensitive which integrates the mislabeled training dataset and noise costs for learning. Finally, the experimental results on the benchmark datasets show the superiority of the proposed method.
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Zhu, W., Yuan, H., Wang, L., Wan, M., Li, X., Ren, J. (2019). A Novel Noise Filter Based on Multiple Voting. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_14
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DOI: https://doi.org/10.1007/978-3-030-24274-9_14
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