Abstract
Graph-based semi-supervised learning (GSSL) is one of the most important semi-supervised learning (SSL) paradigms. Though GSSL methods are helpful in many situations, they may hurt performance when using unlabeled data. In this paper, we propose a new GSSL method GsslIs based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of unlabeled instances, it is not the best to exploit all the unlabeled instances; instead, we should exploit the unlabeled instances which are highly possible to help improve the performance, while do not take the ones with high risk into account. Experiments on a board range of data sets show that the chance of performance degeneration of our proposal is much smaller than that of many state-of-the-art GSSL methods.
This research was supported by NSFC (61403186), JiangsuSF (BK20140613), 863 Program (2015AA015406).
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Wang, H., Wang, SB., Li, YF. (2016). Instance Selection Method for Improving Graph-Based Semi-supervised Learning. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_47
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