Semi-supervised classification method through oversampling and common hidden space

被引:36
作者
Dong, Aimei [1 ,3 ]
Chung, Fu-lai [2 ]
Wang, Shitong [1 ,2 ]
机构
[1] Jiangnan Univ, Sch Digital Media, Wuxi, Jiangsu, Peoples R China
[2] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
[3] Qilu Univ Technol, Sch Informat, Jinan, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
Semi-supervised classification; Oversampling; Common hidden space; Dimensionality augmentation; STATISTICAL COMPARISONS; CLASSIFIERS; FRAMEWORK;
D O I
10.1016/j.ins.2016.02.042
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Semi-supervised classification methods attempt to improve classification performance based on a small amount of labeled data through full use of abundant unlabeled data. Although existing semi-supervised classification methods have exhibited promising results in many applications, they still have drawbacks, including performance degeneration, due to the introduction of unlabeled data and partially false labels in a small amount of labeled data. To circumvent such drawbacks, a new semi-supervised classification method OCHS-SSC through oversampling and a common hidden space is proposed in the paper. The primary characteristics of the proposed method include two aspects. One is that unlabeled data are only used to generate new synthetic data to extend the minimal amount of labeled data. The other is that the final classifier is learned in the extended feature space, which is composed of the original feature space and the common hidden space found between labeled data and the synthetic data instead of the original feature space. Extensive experiments on 23 datasets indicate the effectiveness of the proposed method. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:216 / 228
页数:13
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