[1]张佳欢,李磊军,李美争,等.基于变精度邻域粗糙集的多标记子空间研究[J].南京理工大学学报(自然科学版),2019,43(04):414-422.[doi:10.14177/j.cnki.32-1397n.2019.43.04.006]
 Zhang Jiahuan,Li Leijun,Li Meizheng,et al.Research on multi-label subspace based on variableprecision neighborhood rough sets[J].Journal of Nanjing University of Science and Technology,2019,43(04):414-422.[doi:10.14177/j.cnki.32-1397n.2019.43.04.006]
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基于变精度邻域粗糙集的多标记子空间研究()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
43卷
期数:
2019年04期
页码:
414-422
栏目:
出版日期:
2019-08-24

文章信息/Info

Title:
Research on multi-label subspace based on variableprecision neighborhood rough sets
文章编号:
1005-9830(2019)04-0414-09
作者:
张佳欢12李磊军12李美争34米据生12解 滨34
1.河北师范大学 数学与信息科学学院,河北 石家庄 050024; 2.河北省计算数学与应用重点实验室,河北 石家庄 050024; 3.河北师范大学 信息技术学院,河北 石家庄 050024; 4.河北省网络与信息安全重点实验室,河北 石家庄 050024
Author(s):
Zhang Jiahuan12Li Leijun12Li Meizheng34Mi Jusheng12Xie Bin34
1.College of Mathematics and Information Science,Hebei Normal University,Shijiazhuang 050024,China; 2.Hebei Key Laboratory of Computational Mathematics and Applications,Shijiazhuang 050024,China; 3.College of Information Technology,Hebei Normal University,Shijiazhuang 050024,China; 4.Hebei Key Laboratory of Network and Information Security,Shijiazhuang 050024,China
关键词:
多标记学习 变精度邻域粗糙集 特征选择 集成
Keywords:
multi-label learning feature selection variable precision neighborhood rough set ensemblence
分类号:
O236
DOI:
10.14177/j.cnki.32-1397n.2019.43.04.006
摘要:
多标记学习是目前机器学习中的热点研究问题。本文基于变精度邻域粗糙集探究多标记学习中的特征选择方法,并对所得到的多标记子空间进行了详细的分析。首先提出了基于多标记学习的变精度邻域粗糙集模型,进而给出了一种多标记学习中的特征选择方法。在此基础上,基于不同的精度和邻域能够得到不同的特征选择结果,即不同的特征子空间。该文详细分析了精度和邻域对特征子空间的影响,并将所得到的特征子空间进行集成,详细分析了相应的集成效果。
Abstract:
Multi-label learning is a kind of hotspot problem on machine learning. In this paper,the variable precision neighborhood rough set is applied to feature selection in multi-label learning,and the different feature subspaces are analyzed systematically. Firstly,this paper proposes the variable precision neighborhood rough set model based on multi-label learning,and a feature selection method in multi-label learning is given. After that,different feature selection results,i.e. different feature subspaces,can be obtained based on different precisions and neighborhoods. In this paper,the influence of accuracy and neighborhood on feature subspace is analyzed in detail. The feature subspaces are integrated,and the corresponding performance is also analyzed.

参考文献/References:

[1] Schapire R E,Singer Y. BoosTexter:A boosting-based system for text categorization[J]. Machine Learning,2000,39(2):135-168.
[2]Clare A,King R D. Knowledge discovery in multi-label phenotype data[J]. Lecture Notes in Computer Science,2001,2168:42-53.
[3]Boutell M R,Luo J,Shen X,et al. Learning multi-label scene classification[J]. Pattern Recognition,2004,37(9):1757-1771.
[4]Elisseeff A,Weston J. A kernel method for multi-labelled classification[C]//Proceedings of the 14th International Conference on Neural Information Processing Systems:Natural and Synthetic. Cambridge:MIT Press,2001,681-687.
[5]Zhang Minling,Zhou Zhihua. ML-KNN:A lazy learning approach to multi-label learning[J]. Pattern Recognition,2007,40(7):2038-2048.
[6]Zhang Minling. Ml-rbf:RBF neural networks for multi-label learning[J]. Neural Processing Letters,2009,29(2):61-74.
[7]Zhang Minling,Pena J M,Robles V. Feature selection for multi-label naive Bayes classification[J]. Information Sciences,2009,179(19):3218-3229.
[8]Zhang Yin,Zhou Zhihua. Multilabel dimensionality reduction via dependence maximization[J]. ACM Transactions on Knowledge Discovery from Data,2010,4(3):1-21.
[9]Park C H,Lee M. On applying linear discriminant analysis for multi-labeled problems[J]. Pattern Recognition Letters,2008,29(7):878-887.
[10]Gao Can,Lai Zhihui,Zhou Jie,et al. Maximum deci-sion entropy-based attribute reduction in decision-theoretic rough set model[J]. Knowledge-Based Systems,2018,143:179-191.
[11]Yan Huyong,Zhang Xuerui,Dong Jianhua,et al. Spatial and temporal relation rule acquisition of eutrophication in Da’ning River based on rough set theory[J]. Ecological Indicators,2016,66:180-189.
[12]Wang Qi,Qian Yuhua,Liang Xinyan,et al. Local neighborhood rough set[J]. Knowledge-Based Systems,2018,153:53-64.
[13]Dai Jianhua,Hu Hu,Wu Weizhi,et al. Maximal discernibility pairs based approach to attribute reduction in fuzzy rough sets[J]. IEEE Transactions on Fuzzy Systems,2018,26(4):2174-2187.
[14]Li Hua,Li Deyu,Zhai Yanhui,et al. A novel attribute reduction approach for multi-label data based on rough set theory[J]. Information Sciences,2016,367:827-847.
[15]Lin Yaojin,Hu Qinghua,Liu Jinghua,et al. Multi-label feature selection based on neighborhood mutual information[J]. Applied Soft Computing,2016,38:244-256.
[16]Zhang Yuanjian,Miao Duoqian,Zhang Zhifei,et al. A three-way selective ensemble model for multi-label classification[J]. International Journal of Approximate Reasoning,2018,103:394-413.
[17]王宇,杨志荣,杨习贝. 决策粗糙集属性约简:一种局部视角方法[J]. 南京理工大学学报,2016,40(4):444-449.
Wang Yu,Yang Zhirong,Yang Xibei. Local attribute reduction approach based on decision-theoretic rough set[J]. Journal of Nanjing University of Science and Technology,2016,40(4):444-449.
[18]段洁,胡清华,张灵均,等. 基于邻域粗糙集的多标记分类特征选择算法[J]. 计算机研究与发展,2015,52(1):56-65.
Duan Jie,Hu Qinghua,Zhang Lingjun,et al. Feature selection for multi-label classification based on neighborhood rough sets[]J. Journal of Computer Research and Development,2015,52(1):56-65.
[19]Trohidis K,Tsoumakas G,Kalliris G,et al. Multilabel classification of music into emotion[C]//Proc of the 9th Int Society for Music Information Retrieval. Philadelphia:ISMIR,2008,325-330.
[20]Briggs F,Huang Y,Raich R,et al. The 9th annual MLSP competition:New methods for acoustic classification of multiple simultaneous bird species in a noisy environment[C]//Proc of 2013 IEEE Int Workshop on Machine Learning for Signal Processing. Los Alamitos,CA:IEEE,2013:22-25.
[21]Eduardo C G,Plastino A,Freitas A A. A genetic algorithm for optimizing the label ordering in multi-label classifier chains[C]//Proceedings of the 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. Herndon,VA,USA:IEEE,2013.

备注/Memo

备注/Memo:
收稿日期:2019-04-28 修回日期:2019-05-22
基金项目:国家自然科学基金(61502144; 61573127; 61672206; 71571062); 河北省自然科学基金(F2018205196); 河北省高等学校科学技术研究项目(BJ2019014; QN2017095); 河北省博士后择优资助科研项目(B2016003013); 河北省三三三人才工程培养经费(A2017002112); 河北师范大学博士基金项目(L2017B19)
作者简介:张佳欢(1994-),男,硕士生,主要研究方向:粗糙集理论、多标记学习,E-mail:17319270192@163.com; 通讯作者:李磊军(1985-),男,博士,副教授,主要研究方向:粒计算、集成学习,E-mail:lileijun1985@163.com。
引文格式:张佳欢,李磊军,李美争,等. 基于变精度邻域粗糙集的多标记子空间研究[J]. 南京理工大学学报,2019,43(4):414-422.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2019-09-30