Safe semi-supervised learning: a brief introduction

doi:10.1007/s11704-019-8452-2

Front. Comput. Sci.

2019, Vol. 13

Issue (4) : 669-676 https://doi.org/10.1007/s11704-019-8452-2

REVIEW ARTICLE

Safe semi-supervised learning: a brief introduction

Yu-Feng LI^1,²(

), De-Ming LIANG^1,²

¹. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
². Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China

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Abstract

Semi-supervised learning constructs the predictive model by learning from a few labeled training examples and a large pool of unlabeled ones. It has a wide range of application scenarios and has attracted much attention in the past decades. However, it is noteworthy that although the learning performance is expected to be improved by exploiting unlabeled data, some empirical studies show that there are situations where the use of unlabeled data may degenerate the performance. Thus, it is advisable to be able to exploit unlabeled data safely. This article reviews some research progress of safe semi-supervised learning, focusing on three types of safeness issue: data quality, where the training data is risky or of low-quality;model uncertainty, where the learning algorithm fails to handle the uncertainty during training; measure diversity, where the safe performance could be adapted to diverse measures.

Keywords machine learning semi-supervised learning safe

Corresponding Author(s): Yu-Feng LI

Just Accepted Date: 20 March 2019 Online First Date: 07 May 2019 Issue Date: 29 May 2019

Cite this article:

Yu-Feng LI,De-Ming LIANG. Safe semi-supervised learning: a brief introduction[J]. Front. Comput. Sci., 2019, 13(4): 669-676.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-8452-2
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I4/669

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