Self-Training Algorithm With Block Similar Neighbor Editing

doi:10.1109/ACCESS.2024.3440915

	Self-Training Algorithm With Block Similar Neighbor Editing
	Bai, Wenwang 1; Zhang, Cuihong2 ; Yang, Zhengguo2 ; Yang, He 1
	2024
发表期刊	IEEE ACCESS
卷号	12 页码:110418-110431
摘要	In the real world, there are only a small amount of data with labels. To make full use of the potential structural information of unlabeled data to train a better classifier, researchers have proposed many semi-supervised learning algorithms. Among these algorithms, self-training is one of the most widely used semi-supervised learning frameworks due to its simplicity. How to select high-confidence samples is a crucial step for self-training. If the misclassified samples are selected as high-confidence samples, this error will be amplified in the iterative process, which affects the performance of the final classifier. To alleviate the impact of this problem, this paper proposes a self-training algorithm with block-similar neighbor editing (STBSNE). STBSNE calculates the distance between samples by the block-based dissimilarity measure, which improves the classification performance on high-dimensional data sets. STBSNE defines the block-estimated neighbor relationship, builds the block-estimated neighbor relationship graph, and proposes the block estimated neighbor editing method to identify outliers and noise points, and edits them to improve the quality of the high-confidence sample selected. Experimental results on 16 benchmark data sets verify the superior performance of the proposed STBSNE compared with seven state-of-the-art algorithms.
关键词	Iterative methods Semisupervised learning Training Prototypes Prediction algorithms Noise measurement Euclidean distance Semi-supervised learning self-training classification block similar neighbor data editing
DOI	10.1109/ACCESS.2024.3440915
收录类别	SCIE ; EI
ISSN	2169-3536
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001300976800001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20243316865623
EI主题词	Classification (of information)
EI分类号	716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 723.4.2 Machine Learning ; 751.4 Acoustic Noise ; 903.1 Information Sources and Analysis ; 921.6 Numerical Methods
原始文献类型	Article
EISSN	2169-3536
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/37656
专题	信息工程与人工智能学院
通讯作者	Yang, Zhengguo
作者单位	1.Northwest Normal Univ, Coll Math & Stat, Lanzhou 730070, Gansu, Peoples R China; 2.Lanzhou Univ Finance & Econ, Sch Informat Engn & Artificial Intelligence, Lanzhou 730000, Gansu, Peoples R China
通讯作者单位	兰州财经大学
推荐引用方式 GB/T 7714	Bai, Wenwang,Zhang, Cuihong,Yang, Zhengguo,et al. Self-Training Algorithm With Block Similar Neighbor Editing[J]. IEEE ACCESS,2024,12:110418-110431.
APA	Bai, Wenwang,Zhang, Cuihong,Yang, Zhengguo,&Yang, He.(2024).Self-Training Algorithm With Block Similar Neighbor Editing.IEEE ACCESS,12,110418-110431.
MLA	Bai, Wenwang,et al."Self-Training Algorithm With Block Similar Neighbor Editing".IEEE ACCESS 12(2024):110418-110431.