Multi-label feature selection based on dynamic graph Laplacian

被引：0

作者：

Li Y. ^{[1
,2
]}

Hu L. ^{[1
,2
]}

Zhang P. ^{[1
,2
]}

Gao W. ^{[1
,2
,3
]}

机构：

[1] College of Computer Science and Technology, Jilin University, Changchun

[2] Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun

[3] College of Chemistry, Jilin University, Changchun

来源：

Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 12期

基金：

中国博士后科学基金; 国家重点研发计划;

关键词：

Classification; Dynamic graph Laplacian matrix; Multi-label feature selection; Real-value label;

D O I：

10.11959/j.issn.1000-436x.2020244

中图分类号：

学科分类号：

摘要：

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. © 2020, Editorial Board of Journal on Communications. All right reserved.

引用

页码：47 / 59

页数：12

共 37 条

[1] GUI J, SUN Z N, JIS W, Et al., Feature selection based on structured sparsity: a comprehensive study, IEEE Transactions on Neural Networks and Learning Systems, 28, 7, pp. 1-18, (2016)
[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, 282, pp. 111-135, (2014)
[3] ZHANG M L, ZHOU Z H., A review on multi-label learning algorithms, IEEE Transactions on Knowledge and data Engineering, 26, 8, pp. 1819-1837, (2014)
[4] TSOUMAKAS G, KATAKIS I, VLAHAVAS I., Mining multi-label data, pp. 667-685, (2009)
[5] TSOUMAKAS G, KATAKIS I., Multi-label classification: an overview, International Journal of Data Warehousing and Mining, 3, 3, pp. 1-13, (2007)
[6] KASHEF S, NEZAMABADI-POUR H, NIKPOUR B., Multilabel feature selection: a comprehensive review and guiding experiments, Wiley Interdisciplinary Reviews Data Mining & Knowledge Discovery, 8, 2, pp. 12-40, (2018)
[7] LIU H T, LENG X Y, WANG L L, Et al., A joint embedded multi-label classification algorithm, Acta Automatica Sinica, 45, 10, pp. 1969-1982, (2019)
[8] LI J, CHENG K, WANG S, Et al., Feature selection: a data perspective, ACM Computing Surveys, 50, 6, pp. 1-45, (2018)
[9] SAEYS Y, INZA I, LARRANAGA P., A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 19, pp. 2507-2517, (2007)
[10] LI Z S, LIU Z G., Feature selection algorithm based on XGBoost, Journal on Communications, 40, 10, pp. 101-108, (2019)

← 1 2 3 4 →