Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems

doi:10.1109/ACCESS.2022.3171109

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems
	Huang, Yunhu 1,4; Chen, Dewang 1,2,3; Zhao, Wendi 3; Lv, Yisheng 2
刊名	IEEE ACCESS
	2022
卷号	10 页码:49873-49891
关键词	Computational 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
ISSN号	2169-3536
DOI	10.1109/ACCESS.2022.3171109
通讯作者	Chen, Dewang(dwchen@fjut.edu.cn)
英文摘要	Grid 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.
资助项目	National 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]
WOS关键词	REGULARIZATION ; DROPRULE ; DESIGN ; SYSTEM ; MODEL
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000795629400001
资助机构	National 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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49467]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Chen, Dewang
作者单位	1.Fuzhou Univ, Key Lab Intelligent Metro Univ Fujian Prov, Fuzhou 350118, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Fujian Univ Technol, Sch Transportat, Fuzhou 350118, Peoples R China 4.Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350108, Peoples R China
推荐引用方式 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.