Abstract
Co-training is a famous semi-supervised learning algorithm which can exploit unlabeled data to improve learning performance. Generally it works under a two-view setting (the input examples have two disjoint feature sets in nature), with the assumption that each view is sufficient to predict the label. However, in real-world applications due to feature corruption or feature noise, both views may be insufficient and co-training will suffer from these insufficient views. In this paper, we propose a novel algorithm named Weighted Co-training to deal with this problem. It identifies the newly labeled examples that are probably harmful for the other view, and decreases their weights in the training set to avoid the risk. The experimental results show that Weighted Co-training performs better than the state-of-art co-training algorithms on several benchmarks.
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This work was supported by the NSFC (61673202, 61305067), the Fundamental Research Funds for the Central Universities, and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Xiangyu Guo received his BS in Electronic Engineering from Xidian University, China in 2014. He received the National Scholarship in 2011. Currently he is a master student at the Department of Computer Science and Technology, Nanjing University, China. His research interests include machine learning and data mining.
Wei Wang is an associate professor at Department of Computer Science and Technology, Nanjing University, China. He received his PhD degree from Department of Computer Science and Technology, Nanjing University, China in 2012. His research interest mainly includes computational learning theory, especially in semi-supervised learning and active learning.
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Guo, X., Wang, W. Towards making co-training suffer less from insufficient views. Front. Comput. Sci. 13, 99–105 (2019). https://doi.org/10.1007/s11704-018-7138-5
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DOI: https://doi.org/10.1007/s11704-018-7138-5