科研成果详情

发表状态已发表Published
题名Fuzzy neighborhood-based partial label feature selection via label iterative disambiguation
作者
发表日期2025-04-01
发表期刊International Journal of Approximate Reasoning
ISSN/eISSN0888-613X
卷号179
摘要

Partial label learning is a specific weakly supervised learning framework in which each training sample is associated with a candidate label set in which the ground-truth label is concealed. Feature selection can remove redundant and irrelevant features to improve the generalization performance of the classification model. However, the impact of ambiguous labels is an essential challenge when adopting feature selection for partial label data. In this paper, a novel two-stage feature selection method is proposed, called fuzzy neighborhood-based partial label feature selection with label iterative disambiguation. In the first stage, the proposed method addresses the issue of noise labels by employing a neighborhood-based iterative strategy to enlarge the gap between ground-truth labels and noisy labels. Subsequently, the labeling confidence induced by label disambiguation is utilized to enhance the robustness of feature selection. In the second stage, feature significance is evaluated using three metrics based on fuzzy neighborhoods. Specifically, fuzzy dependency is obtained using fuzzy relations and labeling confidence. Fuzzy neighborhood entropy-based information gain is proposed as an uncertainty measure. Furthermore, the similarity between samples in the same fuzzy neighborhood is used to estimate neighborhood consistency. The fusion of the above metrics can select more discriminative features for partial label learning. Finally, experimental results on eight controlled UCI datasets and five real-world datasets demonstrate that the proposed method achieves superior performance than other compared methods.

关键词Feature selection Fuzzy neighborhood rough sets Granular computing Partial label learning Uncertainty measure
DOI10.1016/j.ijar.2024.109358
URL查看来源
收录类别SCIE
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:001398420400001
Scopus入藏号2-s2.0-85214342030
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/12801
专题北师香港浸会大学
通讯作者Qian, Wenbin
作者单位
1.School of Computer and Information Engineering,Jiangxi Agricultural University,Nanchang,330045,China
2.School of Software,Jiangxi Agricultural University,Nanchang,330045,China
3.Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,China
4.Department of Computer Science,Hong Kong Baptist University,Hong Kong,China
推荐引用方式
GB/T 7714
Li, Junqi,Qian, Wenbin,Yang, Wenjiet al. Fuzzy neighborhood-based partial label feature selection via label iterative disambiguation[J]. International Journal of Approximate Reasoning, 2025, 179.
APA Li, Junqi, Qian, Wenbin, Yang, Wenji, Liu, Suxuan, & Huang, Jintao. (2025). Fuzzy neighborhood-based partial label feature selection via label iterative disambiguation. International Journal of Approximate Reasoning, 179.
MLA Li, Junqi,et al."Fuzzy neighborhood-based partial label feature selection via label iterative disambiguation". International Journal of Approximate Reasoning 179(2025).
条目包含的文件
条目无相关文件。
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[Li, Junqi]的文章
[Qian, Wenbin]的文章
[Yang, Wenji]的文章
百度学术
百度学术中相似的文章
[Li, Junqi]的文章
[Qian, Wenbin]的文章
[Yang, Wenji]的文章
必应学术
必应学术中相似的文章
[Li, Junqi]的文章
[Qian, Wenbin]的文章
[Yang, Wenji]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。