中文版 | English
Title

Class binarization to neuroevolution for multiclass classification

Author
Corresponding AuthorGao, Zhenyu; Liu, Ting
Publication Years
2022-07-01
DOI
Source Title
ISSN
0941-0643
EISSN
1433-3058
Abstract
Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass classification can be categorized as (1) decomposition into binary (2) extension from binary and (3) hierarchical classification. Decomposing multiclass classification into a set of binary classifications that can be efficiently solved by using binary classifiers, called class binarization, which is a popular technique for multiclass classification. Neuroevolution, a general and powerful technique for evolving the structure and weights of neural networks, has been successfully applied to binary classification. In this paper, we apply class binarization techniques to a neuroevolution algorithm, NeuroEvolution of Augmenting Topologies (NEAT), that are used to generate neural networks for multiclass classification. We propose a new method that applies Error-Correcting Output Codes (ECOC) to design the class binarization strategies on the neuroevolution for multiclass classification. The ECOC strategies are compared with the class binarization strategies of One-vs-One and One-vs-All on three well-known datasets of Digit, Satellite, and Ecoli. We analyse their performance from four aspects of multiclass classification degradation, accuracy, evolutionary efficiency, and robustness. The results show that the NEAT with ECOC performs high accuracy with low variance. Specifically, it shows significant benefits in a flexible number of binary classifiers and strong robustness.
Keywords
URL[Source Record]
Indexed By
SCI ; EI
Language
English
SUSTech Authorship
First
Funding Project
Guangdong Natural Science Funds for Young Scholar[2021A1515110641] ; National Natural Science Foundation of China[61773197] ; Shenzhen Fundamental Research Program[JCYJ20200109141622964]
WOS Research Area
Computer Science
WOS Subject
Computer Science, Artificial Intelligence
WOS Accession No
WOS:000822478900001
Publisher
EI Accession Number
20222812340633
EI Keywords
Codes (symbols)
ESI Classification Code
Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Information Sources and Analysis:903.1
ESI Research Field
ENGINEERING
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/355844
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
2.Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands
3.Vrije Univ Amsterdam, Dept Clin Neuro & Dev Psychol, Amsterdam, Netherlands
First Author AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
Lan, Gongjin,Gao, Zhenyu,Tong, Lingyao,et al. Class binarization to neuroevolution for multiclass classification[J]. NEURAL COMPUTING & APPLICATIONS,2022.
APA
Lan, Gongjin,Gao, Zhenyu,Tong, Lingyao,&Liu, Ting.(2022).Class binarization to neuroevolution for multiclass classification.NEURAL COMPUTING & APPLICATIONS.
MLA
Lan, Gongjin,et al."Class binarization to neuroevolution for multiclass classification".NEURAL COMPUTING & APPLICATIONS (2022).
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