[1]严菲,王晓栋.鲁棒的半监督多标签特征选择方法[J].智能系统学报,2019,14(04):812-819.[doi:10.11992/tis.201809017]
 YAN Fei,WANG Xiaodong.A robust, semi-supervised, and multi-label feature selection method[J].CAAI Transactions on Intelligent Systems,2019,14(04):812-819.[doi:10.11992/tis.201809017]
点击复制

鲁棒的半监督多标签特征选择方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年04期
页码:
812-819
栏目:
出版日期:
2019-07-02

文章信息/Info

Title:
A robust, semi-supervised, and multi-label feature selection method
作者:
严菲 王晓栋
厦门理工学院 计算机与信息工程学院, 福建 厦门 361024
Author(s):
YAN Fei WANG Xiaodong
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
关键词:
特征选择半监督学习多标签学习l1范式图线性回归l21范数鲁棒分类聚类
Keywords:
feature selectionsemi-supervised learningmulti-label learningl1-norm graphlinear regressionl21-normrobustclassificationclustering
分类号:
TP391.4
DOI:
10.11992/tis.201809017
摘要:
针对现有的半监督多标签特征选择方法利用l2-范数建立谱图易受到噪声影响的问题,文中提出一种鲁棒的半监督多标签特征选择方法,利用全局线性回归函数建立多标签特征选择模型,结合l1图获取局部描述信息提高模型准确度,引入l2,1约束提升特征之间可区分度和回归分析的稳定性,避免噪声干扰。在4种开源数据集上借助多种性能评价标准验证所提出方法,结果表明:本文方法能有效提高分类模型的准确性和对外界噪声的抗干扰性。
Abstract:
The existing semi-supervised multi-label feature selection method constructs a spectral image based on the l2-norm, which is sensitive to noise. To handle this problem, a robust semi-supervised multi-label feature selection method is presented in this study. A global linear regression function is utilized to construct the multi-label feature selection model, and the l1-norm graph is combined to obtain the local discriminant information. Subsequently, the l2,1-norm constraint is added to improve the distinguishability between these features and the stability of regression analysis to avoid noise interference. Four open source datasets are selected to verify the proposed method based on various evaluation criteria. The results demonstrate the efficiency of our method with respect to the classification accuracy and robustness.

参考文献/References:

[1] YU Lei, LIU Huan. Feature selection for high-dimensional data:a fast correlation-based filter solution[C]//Proceedings of the 20th International Conference on International Conference on Machine Learning. Washington DC, USA, 2003:856-863.
[2] 胡敏杰, 林耀进, 杨红和, 等. 基于特征相关的谱特征选择算法[J]. 智能系统学报, 2017, 12(4):519-525 HU Minjie, LIN Yaojin, YANG Honghe, et al. Spectral feature selection based on feature correlation[J]. CAAI transactions on intelligent systems, 2017, 12(4):519-525
[3] YANG Yi, SHEN Hengtao, MA Zhigang, et al. l2,1-norm regularized discriminative feature selection for unsupervised learning[C]//Proceedings of the 22th International Joint Conference on Artificial Intelligence. Barcelona, Spain, 2011:1589-1594.
[4] WANG Xiaodong, ZHANG Xu, ZENG Zhiqiang, et al. Unsupervised spectral feature selection with l1 norm graph[J]. Neurocomputing, 2016, 200:47-54.
[5] DOQUIRE G, VERLEYSEN M. A graph Laplacian based approach to semi-supervised feature selection for regression problems[J]. Neurocomputing, 2013, 121:5-13.
[6] LIU Yun, NIE Feiping, WU Jigang, et al. Efficient semi-supervised feature selection with noise insensitive trace ratio criterion[J]. Neurocomputing, 2013, 105:12-18.
[7] MA Zhigang, NIE Feiping, YANG Yi, et al. Discriminating joint feature analysis for multimedia data understanding[J]. IEEE transactions on multimedia, 2012, 14(6):1662-1672.
[8] JI Shuiwang, TANG Lei, YU Shipeng, et al. A shared-subspace learning framework for multi-label classification[J]. ACM transactions on knowledge discovery from data, 2010, 4(2):8.
[9] 张俐, 王枞. 基于最大相关最小冗余联合互信息的多标签特征选择算法[J]. 通信学报, 2018, 39(5):111-122 ZHANG Li, WANG Cong. Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy[J]. Journal on communications, 2018, 39(5):111-122
[10] ALALGA A, BENABDESLEM K, TALEB N. Soft-constrained Laplacian score for semi-supervised multi-label feature selection[J]. Knowledge and information systems, 2016, 47(1):75-98.
[11] CHANG Xiaojun, NIE Feiping, YANG Yi, et al. A convex formulation for semi-supervised multi-label feature selection[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec City, Québec, Canada, 2014:1171-1177.
[12] ZHOU Sihang, LIU Xinwang, ZHU Chengzhang, et al. Spectral clustering-based local and global structure preservation for feature selection[C]//Proceedings of 2014 International Joint Conference on Neural Networks. Beijing, China, 2014:550-557.
[13] NIE Feiping, WANG Hua, HUANG Heng, et al. Unsupervised and semi-supervised learning via l1 norm graph[C]//Proceedings of 2011 International Conference on Computer Vision. Barcelona, Spain, 2011:2268-2273.
[14] LIU Yun, GUO Yiming, WANG Hua, et al. Semi-supervised classifications via elastic and robust embedding[C]//Proceedings of the 31th AAAI Conference on Artificial Intelligence. San Francisco, USA, 2017:2294-2300.
[15] SCHÖLKOPF B, PLATT J, HOFMANN T. Multi-instance multi-label learning with application to scene classification[C]//Proceedings of the 13th International Conference on Neural Information Processing Systems. Hong Kong, China, 2006:1609-1616.
[16] ELISSEEFF A, WESTON J. A kernel method for multi-labelled classification[C]//Proceedings of the 14th International Conference on Neural Information Processing Systems. Vancouver, Canada, 2001:681-687.
[17] UEDA N, SAITO K. Parametric mixture models for multi-labeled text[C]//Proceedings of the 15th International Conference on Neural Information Processing Systems. Cambridge, MA, USA, 2002:737-744.
[18] ZHANG Minling, ZHOU Zhihua. ML-KNN:a lazy learning approach to multi-label learning[J]. Pattern recognition, 2007, 40(7):2038-2048.
[19] MARON O, RATAN A L. Multiple-instance learning for natural scene classification[C]//Proceedings of 15th International Conference on Machine Learning. San Francisco, CA, USA, 1998:341-349.

相似文献/References:

[1]孙正兴,张尧烨,李 彬.基于线性规划分类器的相关反馈技术[J].智能系统学报,2007,2(03):34.
 SUN Zheng-xing,ZHANG Yao-ye,LI Bin.Applying relevance feedback with a linear programming classifier[J].CAAI Transactions on Intelligent Systems,2007,2(04):34.
[2]张志飞,苗夺谦.基于粗糙集的文本分类特征选择算法[J].智能系统学报,2009,4(05):453.[doi:10.3969/j.issn.1673-4785.2009.05.011]
 ZHANG Zhi-fei,MIAO Duo-qian.Feature selection for text categorization based on rough set[J].CAAI Transactions on Intelligent Systems,2009,4(04):453.[doi:10.3969/j.issn.1673-4785.2009.05.011]
[3]顾成杰,张顺颐,杜安源.结合粗糙集和禁忌搜索的网络流量特征选择[J].智能系统学报,2011,6(03):254.
 GU Chengjie,ZHANG Shunyi,DU Anyuan.Feature selection of network traffic using a rough set and tabu search[J].CAAI Transactions on Intelligent Systems,2011,6(04):254.
[4]李建元,周脚根,关佶红,等.谱图聚类算法研究进展[J].智能系统学报,2011,6(05):405.
 LI Jianyuan,ZHOU Jiaogen,GUAN Jihong,et al.A survey of clustering algorithms based on spectra of graphs[J].CAAI Transactions on Intelligent Systems,2011,6(04):405.
[5]孙倩茹,王文敏,刘宏.视频序列的人体运动描述方法综述[J].智能系统学报,2013,8(03):189.
 SUN Qianru,WANG Wenmin,LIU Hong.Study of human action representation in video sequences[J].CAAI Transactions on Intelligent Systems,2013,8(04):189.
[6]刘杨磊,梁吉业,高嘉伟,等.基于Tri-training的半监督多标记学习算法[J].智能系统学报,2013,8(05):439.[doi:10.3969/j.issn.1673-4785.201305033]
 LIU Yanglei,LIANG Jiye,GAO Jiawei,et al.Semi-supervised multi-label learning algorithm based on Tri-training[J].CAAI Transactions on Intelligent Systems,2013,8(04):439.[doi:10.3969/j.issn.1673-4785.201305033]
[7]曹晋,张莉,李凡长.一种基于支持向量数据描述的特征选择算法[J].智能系统学报,2015,10(02):215.[doi:10.3969/j.issn.1673-4785.201405063]
 CAO Jin,ZHANG Li,LI Fanzhang.A noval support vector data description-based feature selection method[J].CAAI Transactions on Intelligent Systems,2015,10(04):215.[doi:10.3969/j.issn.1673-4785.201405063]
[8]张佳骕,蒋亦樟,王士同.基于特征选择聚类方法的稀疏TSK模糊系统[J].智能系统学报,2015,10(04):583.[doi:10.3969/j.issn.1673-4785.201412001]
 ZHANG Jiasu,JIANG Yizhang,WANG Shitong.Sparse TSK fuzzy system based on feature selection clustering method[J].CAAI Transactions on Intelligent Systems,2015,10(04):583.[doi:10.3969/j.issn.1673-4785.201412001]
[9]蒋新华,高晟,廖律超,等.半监督SVM分类算法的交通视频车辆检测方法[J].智能系统学报,2015,10(5):690.[doi:10.11992/tis.201406044]
 JIANG Xinhua,GAO Sheng,LIAO Ljuchao,et al.Traffic video vehicle detection based on semi-supervised SVM classification algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(04):690.[doi:10.11992/tis.201406044]
[10]陈玉明,吴克寿,李向军.基因表达数据在邻域关系中的特征选择[J].智能系统学报,2014,9(02):210.[doi:10.3969/j.issn.1673-4785.201307014]
 CHEN Yuming,WU Keshou,LI Xiangjun.Gene expression data feature selection with neighborhood relation[J].CAAI Transactions on Intelligent Systems,2014,9(04):210.[doi:10.3969/j.issn.1673-4785.201307014]

备注/Memo

备注/Memo:
收稿日期:2018-09-13。
基金项目:国家自然科学基金项目(61871464);福建省自然科学基金面上项目(2017J01511);福建省中青年教师科研项目(JAT170417);厦门理工学院科研攀登计划项目(XPDKQ18012).
作者简介:严菲. 女,1985年生,实验师,主要研究方向为特征选择、机器学习。主持福建省教育厅中青年教师项目1项。发表学术论文5篇;王晓栋,男,1983年生,副教授,博士,主要研究方向为机器学习、图像处理。主持福建省自然科学基金面上项目1项,福建教育厅中青年教师项目1项。发表学术论文10篇。
通讯作者:严菲.E-mail:fyan@xmut.edu.cn
更新日期/Last Update: 2019-08-25