[1]潘思远,刘园奎,毛 煜,等.基于邻域决策误差率的多标记特征选择[J].南京师范大学学报(工程技术版),2023,23(01):066-74.[doi:10.3969/j.issn.1672-1292.2023.01.009]
　Pan Siyuan,Liu Yuankui,Mao Yu,et al.Multi-Label Feature Selection Based on Neighborhood Approximation Error Rate[J].Journal of Nanjing Normal University(Engineering and Technology),2023,23(01):066-74.[doi:10.3969/j.issn.1672-1292.2023.01.009]
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基于邻域决策误差率的多标记特征选择
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
23卷
期数:
2023年01期
页码:
066-74
栏目:
计算机科学与技术
出版日期:
2023-03-15

文章信息/Info

Title:
Multi-Label Feature Selection Based on Neighborhood Approximation Error Rate
文章编号:
1672-1292(2023)01-0066-09
作者:
潘思远1; 2; 刘园奎1; 2; 毛 煜1; 2; 林耀进1; 2
(1.闽南师范大学计算机学院,福建 漳州 363000) (2.闽南师范大学计算机学院数据科学与智能应用福建省高等学校重点实验室,福建 漳州 363000)
Author(s):
Pan Siyuan1; 2; Liu Yuankui1; 2; Mao Yu1; 2; Lin Yaojin1; 2
(1.School of Computer Science,Minnan Normal University,Zhangzhou 363000,China) (2.Fujian Key Laboratory of Granular Computing and Application,School of Computer Science,Minnan Normal University,Zhangzhou 363000,China)
关键词:
多标记学习; 特征选择; 邻域近似误差率
Keywords:
multi-label learning; feature selection; neighborhood approximation error rate
分类号:
O643/X703
DOI:
10.3969/j.issn.1672-1292.2023.01.009
文献标志码:
A
摘要:
多标记学习可以同时处理与一组标记相关的数据,多标记学习的研究对于多义性对象的学习建模具有十分重要的意义. 与传统的单标记学习一样,数据的高维性是多标记学习的阻碍,因此数据降维是一项十分重要的工作,而特征选择是一种有效的数据降维技术. 提出了基于邻域近似误差率的多标记特征选择算法. 首先,在邻域粗糙集理论的基础上,引入实例的边界来对所有实例进行粒度化. 其次,基于邻域决策误差率提出了邻域近似误差率的策略来评价特征. 最后,在公开的数据集上进行了大量的实验,结果表明所提算法的有效性.
Abstract:
Multi-label learning can process data associated with a set of labels simultaneously, and the study of multi-label learning is very important for learning modeling of polysemous objects. As with traditional single-label learning, the high dimensionality of data is an obstacle to multi-label learning, so data dimensionality reduction is a very important task, and feature selection is an effective data dimensionality reduction technique. A multi-label feature selection algorithm is proposed on the basis of the neighborhood approximation error rate. Firstly, based on the neighborhood rough set theory, the boundaries of instances are introduced to granularize all instances. Secondly, a neighborhood approximation error rate strategy is proposed to evaluate features based on the neighborhood decision error rate. Finally, extensive experiments are conducted on publicly available datasets.The results show the effectiveness of the proposed algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2022-09-15.
基金项目:国家自然科学基金项目(62076116)、福建省自然科学基金项目(2020J01811、2020J01792、2021J02049).
通讯作者:林耀进,博士,教授,研究方向:数据挖掘,粒计算. E-mail:zzlinyaojin@163.com
更新日期/Last Update: 2023-03-15