| 一种基于邻域粗糙集的多标记专属特征选择方法 |
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| 引用本文： | 孙林,潘俊方,张霄雨,王伟,徐久成.一种基于邻域粗糙集的多标记专属特征选择方法[J].计算机科学,2018,45(1):173-178. |
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| 作者姓名： | 孙林 潘俊方 张霄雨 王伟 徐久成 |
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| 作者单位： | 计算智能与数据挖掘河南省高校工程技术研究中心 河南 新乡453007,电子科技大学基础与前沿研究院 成都610054,河南师范大学计算机与信息工程学院 河南 新乡453007,河南师范大学计算机与信息工程学院 河南 新乡453007,河南师范大学计算机与信息工程学院 河南 新乡453007;河南师范大学生命科学学院生物学博士后流动站 河南 新乡453007 |
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| 基金项目： | 本文受国家自然科学基金项目(61772176,61402153,9,61602158),中国博士后科学基金项目(2016M602247),河南省科技攻关项目(162102210261),新乡市科技攻关计划项目(CXGG17002),河南师范大学博士科研启动费支持课题(qd15132)资助 |
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| 摘 要： | 在多标记学习中,数据降维是一项重要且具有挑战性的任务,而特征选择又是一种高效的数据降维技术。在邻域粗糙集理论的基础上提出一种多标记专属特征选择方法,该方法从理论上确保了所得到的专属特征与相应标记具有较强的相关性,进而改善了约简效果。首先,该方法运用粗糙集理论的约简算法来减少冗余属性,在保持分类能力不变的情况下获得标记的专属特征；然后,在邻域精确度和邻域粗糙度概念的基础上,重新定义了基于邻域粗糙集的依赖度与重要度的计算方法,探讨了该模型的相关性质；最后,构建了一种基于邻域粗糙集的多标记专属特征选择模型,实现了多标记分类任务的特征选择算法。在多个公开的数据集上进行仿真实验,结果表明了该算法是有效的。
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| 关 键 词： | 多标记学习 邻域粗糙集 专属特征 特征选择 |
| 收稿时间： | 2017/5/8 0:00:00 |
| 修稿时间： | 2017/9/16 0:00:00 |
Multi-label-specific Feature Selection Method Based on Neighborhood Rough Set |
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| SUN Lin,PAN Jun-fang,ZHANG Xiao-yu,WANG Wei and XU Jiu-cheng.Multi-label-specific Feature Selection Method Based on Neighborhood Rough Set[J].Computer Science,2018,45(1):173-178. |
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| Authors: | SUN Lin PAN Jun-fang ZHANG Xiao-yu WANG Wei and XU Jiu-cheng |
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| Affiliation: | College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China;Post-doctoral Mobile Station of Biology,College of Life Science,Henan Normal University,Xinxiang,Henan 453007,China;Engineering Technology Research Center for Computing Intelligence and Data Mining of Henan Province,Xinxiang,Henan 453007,China,Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 610054,China,College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China,College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China and College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China;Post-doctoral Mobile Station of Biology,College of Life Science,Henan Normal University,Xinxiang,Henan 453007,China |
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| Abstract: | Dimensionality reduction of data is a significant and challenging task under multi-label learning,and feature selection is a valid technology to reduce the dimension of vector.In this paper,a multi-label-specific feature selection method based on neighborhood rough set theory was proposed.This method ensures theoretically that there exists a strong correlation between the obtained label-specific features and the corresponding labels,and then reduction efficiency can be improved well.Firstly,a reduction algorithm of rough set theory is applied to reduce redundant attributes,and the label-specific features are obtained while keeping the classification ability unchanged.Then,the concepts of neighborhood accuracy and neighborhood roughness are introduced,the calculation approaches to dependence and attribute significance based on neighborhood rough set are redefined,and the related properties of this model are discussed.Finally,a multi-label-specific feature selection model based on neighborhood rough set is presented,and the corresponding feature selection algorithm for multi-label classification task is designed.The experimental results under some public datasets demonstrate the effectiveness of the proposed multi-label-specific feature selection method. |
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| Keywords: | Multi-label learning Neighborhood rough set Label-specific feature Feature selection |
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