Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems
Huang, Yunhu1,4; Chen, Dewang2,3,4; Zhao, Wendi2; Lv, Yisheng3
Source PublicationIEEE ACCESS
ISSN2169-3536
2022
Volume10Pages:49873-49891
Corresponding AuthorChen, Dewang(dwchen@fjut.edu.cn)
AbstractGrid partitioning for input space results in the exponential rise in the number of rules in adaptive network-based fuzzy inference system (ANFIS) and patch learning (PL) as the number of features increases, thus resulting in the huge computational load and deteriorating its interpretability. An improved PL (iPL) is put forward for the training of each sub-fuzzy system to overcome the rule-explosion problem. In the iPL, input partitioning is done using fuzzy c-means (FCM) clustering to avoid the heavy computational complexity arising due to the large number of rules generated from high dimensionality. In this paper, two novel classifiers, called FCM clustering based deep patch learning with improved high-level interpretability for classification problems, are presented, named as HI-FCMDPL-CP1 and HI-FCMDPL-CP2. The proposed classifiers have two characteristics: One is a stacked deep structure of component iPL fuzzy classifiers for high accuracy, and the other is the use of maximal information coefficient (MIC) and the maximum misclassification threshold (MMT) to optimize the deep structures. High interpretability is achieved at each layer by using the FCM clustering, concise structure and large input dimensionality. The MMT, random input (RI) and parameter sharing (PS) are integrated to improve their classification accuracy without losing their interpretability. Experiments on several real-word datasets demonstrated that MIC, RI and PS in HI-FCMDPL-CP1 and HI-FCMDPL-CP2 are effective individually, and integrating them all three can further improve the classification performance. A more concise deep fuzzy system is obtained with the number of features and fuzzy rules reduced simultaneously. Furthermore, MIC, RI and PS are used to determine the advantages and disadvantages of using serial versus parallel structures to avoid subjective selection of these two categories.
KeywordComputational modeling Training Microwave integrated circuits Deep learning Data models Artificial neural networks Training data Fuzzy c-means (FCM) clustering maximal information coefficient (MIC) random input (RI) deep patch learning classifier interpretability
DOI10.1109/ACCESS.2022.3171109
WOS KeywordREGULARIZATION ; DROPRULE ; DESIGN ; SYSTEM ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61976055] ; Special Fund for Education and Scientific Research of Fujian Provincial Department of Finance[GY-Z21001] ; State Key Laboratory for Management and Control of Complex Systems[20210116]
Funding OrganizationNational Natural Science Foundation of China ; Special Fund for Education and Scientific Research of Fujian Provincial Department of Finance ; State Key Laboratory for Management and Control of Complex Systems
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000795629400001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49468
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorChen, Dewang
Affiliation1.Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350108, Peoples R China
2.Fujian Univ Technol, Sch Transportat, Fuzhou 350118, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Fuzhou Univ, Key Lab Intelligent Metro Univ Fujian Prov, Fuzhou 350118, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Huang, Yunhu,Chen, Dewang,Zhao, Wendi,et al. Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems[J]. IEEE ACCESS,2022,10:49873-49891.
APA Huang, Yunhu,Chen, Dewang,Zhao, Wendi,&Lv, Yisheng.(2022).Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems.IEEE ACCESS,10,49873-49891.
MLA Huang, Yunhu,et al."Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems".IEEE ACCESS 10(2022):49873-49891.
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