Instance selection method for improving graph-based semi-supervised learning

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外文摘要：Graph-based semi-supervised learning is an important semi-supervised learning paradigm.Although graphbased semi-supervised learning methods have been shown to be helpful in various situations,they may adversely affect performance when using unlabeled data.In this paper,we propose a new graph-based semi-supervised learning method 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 approach to exploit all the unlabeled instances;instead,we should exploit the unlabeled instances that are highly likely to help improve the performance,while not taking into account the ones with high risk.We develop both transductive and inductive variants of our method.Experiments on a broad range of data sets show that the chances of performance degeneration of our proposed method are much smaller than those of many state-of-the-art graph-based semi-supervised learning methods.

外文关键词：

graph-based semi-supervised learningperformance degenerationinstance selection

作者：

Hai WANG、Shao-Bo WANG、Yu-Feng LI

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作者单位：

National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China

Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China

基金：

authors want to thank the associate editors and reviewers for helpful comments and suggestionsThis research was partially supported by the National Natural Science Foundation of ChinaJiangsu Science FoundationMSRA research fund

项目编号：

61403186BK20140613

出版年：

2018

计算机科学前沿

高等教育出版社

计算机科学前沿

CSCDSCIEI

影响因子：0.303

ISSN：2095-2228

年,卷(期)：2018.12(4)

被引量3
参考文献量1