基于动态图拉普拉斯的多标签特征选择

doi:10.11959/j.issn.1000-436X.2020244

Abstract

Abstract:

In view of the problems that graph-based multi-label feature selection methods ignore the dynamic change of graph Laplacian matrix, as well as such methods employ logical-value labels to guide feature selection process and loses label information, a multi-label feature selection method based on both dynamic graph Laplacian matrix and real-value labels was proposed.The robust low-dimensional space of feature matrix was used to construct a dynamic graph Laplacian matrix, and the robust low-dimensional space was used as the real-value label space.Furthermore, manifold and non-negative constraints were adopted to transform logical labels into real-valued labels to address the issues mentioned above.The proposed method was compared to three multi-label feature selection methods on nine multi-label benchmark data sets in experiments.The experimental results demonstrate that the proposed multi-label feature selection method can obtain the higher quality feature subset and achieve good classification performance.

Key words: multi-label feature selection, dynamic graph Laplacian matrix, real-value label, classification

CLC Number:

TP18

Yonghao LI, Liang HU, Ping ZHANG, Wanfu GAO. Multi-label feature selection based on dynamic graph Laplacian[J]. Journal on Communications, 2020, 41(12): 47-59.

Figures/Tables 11

数据集	所提方法	MIFS	RALM-FS	SCLS
Arts	$0 . 158 \pm 0 . 076$	0.139±0.078	0.106±0.046	0.102±0.061
Birds	$0 . 123 \pm 0 . 058$	0.116±0.059	0.060±0.040	0.096±0.046
Yeast	$0 . 564 \pm 0 . 036$	0.547±0.035	0.532±0.008	0.552±0.027
Education	$0 . 261 \pm 0 . 061$	0.073±0.059	0.073±0.059	0.193±0.056
Enron	$0 . 524 \pm 0 . 051$	0.372±0.027	0.488±0.031	0.389±0.059
Social	$0 . 468 \pm 0 . 118$	0.276±0.136	0.363±0.120	0.149±0.112
Science	$0 . 141 \pm 0 . 053$	0.129±0.057	0.037±0.035	0.097±0.054
Entertain	$0 . 283 \pm 0 . 101$	0.228±0.112	0.113±0.072	0.214±0.100
Society	$0 . 317 \pm 0 . 042$	0.300±0.042	0.216±0.028	0.223±0.059
平均值	$0 . 315$	0.242	0.221	0.224

数据集	所提方法	MIFS	RALM-FS	SCLS
Arts	$0 . 067 \pm 0 . 038$	0.055±0.034	0.038±0.016	0.039±0.026
Birds	$0 . 077 \pm 0 . 037$	0.075±0.036	0.039±0.026	0.058±0.024
Yeast	$0 . 243 \pm 0 . 048$	0.219±0.048	0.207±0.014	0.229±0.036
Education	$0 . 068 \pm 0 . 015$	0.019±0.017	0.019±0.015	0.052±0.014
Enron	$0 . 135 \pm 0 . 036$	0.074±0.017	0.119±0.031	0.074±0.027
Social	$0 . 054 \pm 0 . 019$	0.031±0.016	0.036±0.014	0.014±0.012
Science	$0 . 056 \pm 0 . 025$	0.034±0.016	0.007±0.007	0.036±0.020
Entertain	$0 . 119 \pm 0 . 039$	0.097±0.047	0.044±0.030	0.081±0.038
Society	$0 . 060 \pm 0 . 016$	0.055±0.020	0.021±0.003	0.032±0.012
平均值	$0 . 097$	0.073	0.059	0.068

数据集	所提方法	MIFS	RALM-FS	SCLS
Arts	$0.212 \pm 0.052$	0.202±0.052	0.164±0.024	0.182±0.043
Birds	0.135±0.061	0.167±0.061	0.144±0.043	$0 . 171 \pm 0 . 066$
Yeast	$0 . 532 \pm 0 . 06$	0.525±0.053	0.518±0.035	0.529±0.019
Education	$0 . 289 \pm 0 . 043$	0.183±0.055	0.176±0.037	0.260±0.050
Enron	$0 . 488 \pm 0 . 033$	0.410±0.024	0.437±0.026	0.365±0.073
Social	$0 . 456 \pm 0 . 061$	0.338±0.081	0.376±0.057	0.315±0.054
Science	$0 . 188 \pm 0 . 028$	0.171±0.037	0.115±0.016	0.160±0.048
Entertain	$0 . 314 \pm 0 . 062$	0.276±0.065	0.234±0.033	0.273±0.065
Society	$0 . 313 \pm 0 . 046$	0.305±0.043	0.245±0.021	0.255±0.050
平均值	$0 . 325$	0.286	0.268	0.279

数据集	所提方法	MIFS	RALM-FS	SCLS
Arts	$0 . 096 \pm 0 . 043$	0.095±0.033	0.074±0.014	0.071±0.025
Birds	0.085±0.038	$0 . 106 \pm 0 . 042$	0.078±0.028	0.093±0.036
Yeast	$0 . 322 \pm 0 . 055$	0.282±0.057	0.301±0.026	0.300±0.027
Education	$0 . 094 \pm 0 . 018$	0.043±0.018	0.059±0.021	0.084±0.023
Enron	$0 . 129 \pm 0 . 018$	0.087±0.014	0.111±0.013	0.081±0.026
Social	$0 . 067 \pm 0 . 020$	0.051±0.017	0.049±0.013	0.038±0.012
Science	$0 . 079 \pm 0 . 021$	0.062±0.015	0.037±0.010	0.057±0.021
Entertain	$0 . 155 \pm 0 . 039$	0.138±0.042	0.112±0.020	0.131±0.040
Society	0.088±0.020	$0 . 089 \pm 0 . 021$	0.055±0.011	0.053±0.016
平均值	$0 . 124$	0.106	0.097	0.101

References 38

[1]	GUI J , SUN Z N , JIS W ,et al. Feature selection based on structured sparsity:a comprehensive study[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016,28(7): 1-18.
[2]	BOLON C N , SANCHEZ M N , ALONSO B A ,et al. A review of microarray datasets and applied feature selection methods[J]. Information Sciences, 2014,282: 111-135.
[3]	ZHANG M L , ZHOU Z H . A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and data Engineering, 2014,26(8): 1819-1837.
[4]	TSOUMAKAS G , KATAKIS I , VLAHAVAS I . Mining multi-label data[M]. Berlin:Springer. 2009: 667-685.
[5]	TSOUMAKAS G , KATAKIS I . Multi-label classification:an overview[J]. International Journal of Data Warehousing and Mining, 2007,3(3): 1-13.
[6]	KASHEF S , NEZAMABADI-POUR H , NIKPOUR B . Multilabel feature selection:a comprehensive review and guiding experiments[J]. Wiley Interdisciplinary Reviews Data Mining ＆ Knowledge Discovery, 2018,8(2): 12-40.
[7]	刘慧婷, 冷新杨, 王利利 ,等. 联合嵌入式多标签分类算法[J]. 自动化学报, 2019,45(10): 1969-1982.
	LIU H T , LENG X Y , WANG L L ,et al. A joint embedded multi-label classification algorithm[J]. Acta Automatica Sinica, 2019,45(10): 1969-1982.
[8]	LI J , CHENG K , WANG S ,et al. Feature selection:a data perspective[J]. ACM Computing Surveys, 2018,50(6): 1-45.
[9]	SAEYS Y , INZA I , LARRA?AGA P . A review of feature selection techniques in bioinformatics[J]. Bioinformatics, 2007,23(19): 2507-2517.
[10]	李占山, 刘兆赓 . 基于 XGBoost 的特征选择算法[J]. 通信学报, 2019,40(10): 101-108.
	LI Z S , LIU Z G . Feature selection algorithm based on XGBoost[J]. Journal on Communications, 2019,40(10): 101-108.
[11]	ZHANG P , LIU G X , GAO W F . Distinguishing two types of labels for multi-label feature selection[J]. Pattern Recognition, 2019,95(1): 72-82.
[12]	CAI Z , ZHU W . Multi-label feature selection via feature manifold learning and sparsity regularization[J]. International Journal of Machine Learning ＆ Cybernetics, 2018,9(8): 1321-1334.
[13]	RODRIGUES D , PEREIRA L A , NAKAMURA R Y ,et al. A wrapper approach for feature selection based on bat algorithm and optimum-path forest[J]. Expert Systems with Applications, 2014,41(5): 2250-2258.
[14]	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): 115-126.
	ZHANG L , WANG C . Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy[J]. Journal on Communications, 2018,39(5): 115-126.
[15]	ZHANG J , LUO Z , LI C ,et al. Manifold regularized discriminative feature selection for multi-label learning[J]. Pattern Recognition, 2019,95(1): 136-150.
[16]	HUANG J , NIE F P , HUANG H ,et al. Robust manifold nonnegative matrix factorization[J]. ACM Transactions on Knowledge Discovery from Data, 2014,8(3): 1-21.
[17]	JIAN L , LI J , SHU K ,et al. Multi-label informed feature selection[C]// Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2016: 1627-1633.
[18]	NIE F P , HUANG H , CAI X ,et al. Efficient and robust feature selection via joint ?2,1-norms minimization[C]// Proceedings of the Advances in neural Information Processing Systems. Massachusetts:MIT Press, 2010: 1813-1821.
[19]	CHANG X , NIE F P , YANG Y ,et al. A convex formulation for semi-supervised multi-label feature selection[C]// Proceedings of the National Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2014: 1171-1177.
[20]	BOUTELL M R , LUO J , SHEN X ,et al. Learning multi-label scene classification[J]. Pattern Recognition, 2004,37(9): 1757-1771.
[21]	FüRNKRANZ J , HüLLERMEIER E , MENCíA E L ,et al. Multilabel classification via calibrated label ranking[J]. Machine Learning, 2008,73(2): 133-153.
[22]	QI G J , HUA X S , RUI Y ,et al. Correlative multi-label video annotation[C]// Proceedings of the 15th International Conference on Multimedia. New York:ACM Press, 2007: 24-29.
[23]	HUANG J , LI G , HUANG Q ,et al. Learning label-specific features and class-dependent labels for multi-label classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2016,28(12): 3309-3323.
[24]	ZHANG Y , YANG Y , LI T ,et al. A multitask multiview clustering algorithm in heterogeneous situations based on LLE and LE[J]. Knowledge-Based Systems, 2019,163(1): 776-786.
[25]	REN Y , ZHANG G , YU G ,et al. Local and global structure preserving based feature selection[J]. Neurocomputing, 2012,89(1): 147-157.
[26]	HUANG R , JIANG W , SUN G . Manifold-based constraint Laplacian score for multi-label feature selection[J]. Pattern Recognition Letters, 2018,112(1): 346-352.
[27]	XU Y , WANG J , AN S ,et al. Semi-supervised multi-label feature selection by preserving feature-label space consistency[C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2018: 783-792.
[28]	CHEN G , SONG Y , WANG F ,et al. Semi-supervised multi-label learning by solving a Sylvester equation[C]// Proceedings of the 2008 SIAM International Conference on Data Mining. Saarland:DBLP, 2008: 410-419.
[29]	SHAO R , XU N , GENG X . Multi-label learning with label enhancement[C]// Proceedings of the 2018 IEEE International Conference on Data Mining. Piscataway:IEEE Press, 2018: 437-446.
[30]	LEE J , KIM D W . SCLS:multi-label feature selection based on scalable criterion for large label set[J]. Pattern Recognition, 2017,66(1): 342-352.
[31]	CAI X , NIE F P , HUANG H . Exact top-k feature selection via l2,0-norm constraint[C]// Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2013: 1240-1246.
[32]	ZHU Y , KWOK J T , ZHOU Z H . Multi-label learning with global and local label correlation[J]. IEEE Transactions on Knowledge and Data Engineering, 2018,30(6): 1081-1094.
[33]	YAN H , YANG J , YANG J . Robust joint feature weights learning framework[J]. IEEE Transactions on Knowledge and Data Engineering, 2016,28(5): 1327-1339.
[34]	DEMPSTER A P , LAIRD N M , RUBIN D B . Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society:Series B (Methodological), 1977,39(1): 1-22.
[35]	LEE D D , SEUNG H S . Algorithms for non-negative matrix factorization[C]// Proceedings of the Advances in Neural Information Processing Systems. Massachusetts:MIT Press, 2001: 556-562.
[36]	CAI D , HE X , HAN J ,et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010,33(8): 1548-1560.
[37]	TSOUMAKAS G , SPYROMITROS-XIOUFIS E , VILCEK J ,et al. Mulan:a Java library for multi-label learning[J]. Journal of Machine Learning Research, 2011,12(7): 2411-2414.
[38]	YU K , YU S , TRESP V . Multi-label informed latent semantic indexing[C]// Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2005: 258-265.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	样例数/个	特征数/个	标签数/个	训练样例数/个	测试样例数/个	领域
Arts	5 000	462	26	2 000	3 000	文本(Web)
Birds	645	260	19	322	323	声音
Yeast	2 417	103	14	1 500	917	生物
Education	5 000	550	33	2 000	3 000	文本(Web)
Enron	1 702	1 001	53	1 123	579	文本
Social	5 000	1 047	39	2 000	3 000	文本(Web)
Science	5 000	743	40	2 000	3 000	文本(Web)
Entertain	5 000	636	27	2 000	3 000	文本(Web)
Society	5 000	640	21	2 000	3 000	文本(Web)

Multi-label feature selection based on dynamic graph Laplacian

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 38

Related Articles 15

Metrics

Recommended 0

[1]	Yifeng WANG, Yuanbo GUO, Qingli CHEN, Chen FANG, Renhao LIN, Yongliang ZHOU, Jiali MA. Method based on contrastive incremental learning for fine-grained malicious traffic classification [J]. Journal on Communications, 2023, 44(3): 1-11.
[2]	Jie YANG, Biao DONG, Xue FU, Yu WANG, Guan GUI. Lightweight decentralized learning-based automatic modulation classification method [J]. Journal on Communications, 2022, 43(7): 134-142.
[3]	Hongping YAN, Qiang ZHOU, Shihao WANG, Wang YAO, Liukun HE, Liangmin WANG. Composite Tor traffic features extraction method of webpage in actual network flow based on SDN [J]. Journal on Communications, 2022, 43(3): 76-87.
[4]	Yifeng WANG, Yuanbo GUO, Qingli CHEN, Chen FANG, Renhao LIN. Method based on contrastive learning for fine-grained unknown malicious traffic classification [J]. Journal on Communications, 2022, 43(10): 12-25.
[5]	Jinbo XIONG, Yongjie ZHOU, Renwan BI, Liang WAN, Youliang TIAN. Towards edge-collaborative, lightweight and privacy-preserving classification framework [J]. Journal on Communications, 2022, 43(1): 127-137.
[6]	Zhiwang CHEN, Zhongxin ZHANG, Juan SONG, Haipeng LEI, Yong PENG. Tracking algorithm of Siamese network based on online target classification and adaptive template update [J]. Journal on Communications, 2021, 42(8): 151-163.
[7]	Hongmin GAO, Xueying CAO, Zhonghao CHEN, Zaijun HUA, Chenming LI, Yue CHEN. Hyperspectral image classification method based on multi-scale proximal feature concatenate network [J]. Journal on Communications, 2021, 42(2): 92-102.
[8]	Liu LIU, Jianhua ZHANG, Yuanyuan FAN, Li YU, Jiachi ZHANG. Survey of application of machine learning in wireless channel modeling [J]. Journal on Communications, 2021, 42(2): 134-153.
[9]	Chuanhong LIU, Caili GUO, Yang YANG, Chunyan FENG, Qizheng SUN, Jiujiu CHEN. Intelligent task-oriented semantic communication method in artificial intelligence of things [J]. Journal on Communications, 2021, 42(11): 97-108.
[10]	Youhua FU, Dong CHEN. Channel estimation for hybrid intelligent reflecting surface structure assisted mmWave communications [J]. Journal on Communications, 2021, 42(10): 189-196.
[11]	Yongjin HU,Yuanbo GUO,Jun MA,Han ZHANG,Xiuqing MAO. Method to generate cyber deception traffic based on adversarial sample [J]. Journal on Communications, 2020, 41(9): 59-70.
[12]	Chunxiang GU,Weisen WU,Ya’nan SHI,Guangsong LI. Method of unknown protocol classification based on autoencoder [J]. Journal on Communications, 2020, 41(6): 88-97.
[13]	Xiaohu ZHAO,Gang WANG,Boming SONG,Jiacheng YU. Multi-source information transmission and classification algorithm for equipment based on compressed sensing [J]. Journal on Communications, 2020, 41(2): 13-24.
[14]	Hongmin GAO,Xueying CAO,Yao YANG,Zaijun HUA,Chenming LI. Application of bilateral fusion model based on CNN in hyperspectral image classification [J]. Journal on Communications, 2020, 41(11): 132-140.
[15]	Ze ZHAO,Yuan GAO,Li CUI. VehLoc: an in-vehicle high-precision location method based on BLE multi-channel RSSI values [J]. Journal on Communications, 2018, 39(12): 47-59.