Group feature selection with multiclass support vector machine

Tang, Fengzhen1; Adam, Lukas2; Si, Bailu1

2018-11-23

10.1016/j.neucom.2018.07.012

NEUROCOMPUTING   影响因子和分区

0925-2312

1872-8286

Feature reduction is nowadays an important topic in machine learning as it reduces the complexity of the final model and makes it easier to interpret. In some applications, the features arise from multiple sources and it is not so important to select the individual features as to select the important sources. This leads to a group feature selection problem. In this paper, we consider the group feature selection in the multiclass classification setting based on the framework of support vector machines. We reformulate it as a sparse problem by prescribing the maximum number of active groups and propose a novel method based on the ADMM algorithm. We proposed the method in such a way that the main computational load is performed in the first iteration and the remaining iterations can be computed fast. This allows us to handle large problems. We demonstrate the good performance of our method on several real-world datasets. (C) 2018 Elsevier B.V. All rights reserved.

Group feature selection Support vector machine Multiclass support vector machine Alternating direction method of multipliers EEG channel selection

SCI ; EI

英语

通讯

National Natural Science Foundation of China[61329302]

Computer Science

Computer Science, Artificial Intelligence

WOS:000444237900004

ELSEVIER SCIENCE BV

20183605788718

Classification (of information) ; Iterative methods ; Support vector machines ; Vectors

Information Theory and Signal Processing:716.1 ; Computer Software, Data Handling and Applications:723 ; Algebra:921.1 ; Numerical Methods:921.6

COMPUTER SCIENCE

Web of Science

1.Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, 114 Nanta St, Shenyang 110016, Liaoning, Peoples R China
2.Southern Univ Sci & Technol, 1088 Xueyuan Ave, Shenzhen 518055, Guangdong, Peoples R China

Tang, Fengzhen,Adam, Lukas,Si, Bailu. Group feature selection with multiclass support vector machine[J]. NEUROCOMPUTING,2018,317:42-49.

Tang, Fengzhen,Adam, Lukas,&Si, Bailu.(2018).Group feature selection with multiclass support vector machine.NEUROCOMPUTING,317,42-49.

Tang, Fengzhen,et al."Group feature selection with multiclass support vector machine".NEUROCOMPUTING 317(2018):42-49.