Deep patch learning algorithms with high interpretability for regression problems

doi:10.1002/int.22937

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Deep patch learning algorithms with high interpretability for regression problems
	Huang, Yunhu 1,2; Chen, Dewang 2,3,4; Zhao, Wendi 2,3; Lv, Yisheng4 ; Wang, Shiping 1,5
发表期刊	INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
ISSN	0884-8173
	2022-06-14
页码	38
通讯作者	Chen, Dewang(dwchen@fjut.edu.cn)
摘要	Improving the performance of machine learning algorithms to overcome the curse of dimensionality while maintaining interpretability is still a challenging issue for researchers in artificial intelligence. Patch learning (PL), based on the improved adaptive network-based fuzzy inference system (ANFIS) and continuous local optimization for the input domain, is characterized by high accuracy. However, PL can only handle low-dimensional data set regression. Based on the parallel and serial ensembles, two deep patch learning algorithms with embedded adaptive fuzzy systems (DPLFSs) are proposed in this paper. First, using the maximum information coefficient (MIC) and Pearson's correlation coefficients for feature selection, the variables with the least relationship (linear or nonlinear) are excluded. Second, principal component analysis is used to reduce the complexity further of DPLFSs. Meanwhile, fuzzy C-means clustering is used to enhance the interpretability of DPLFSs. Then, an improved PL method is put forward for the training of each sub-fuzzy system in a fashion of bottom-up layer-by-layer, and finally, the structure optimization is performed to significantly improve the interpretability of DPLFSs. Experiments on several benchmark data sets show the advantages of a DPLFS: (1) it can handle medium-scale data sets; (2) it can overcome the curse of dimensionality faced by PL; (3) its precision and generalization are greatly improved; and (4) it can overcome the poor interpretability of deep learning networks. Compared with shallow and deep learning algorithms, DPLFSs have the advantages of interpretability, self-learning, and high precision. DPLFS1 is superior for medium-scale data; DPLFS2 is more efficient and effective for high-dimensional problems, has a faster convergence, and is more interpretable.
关键词	deep learning deep patch learning fuzzy system fuzzy C-means clustering interpretability maximum information coefficient (MIC) Pearson's correlation coefficients (PCC)
DOI	10.1002/int.22937
关键词[WOS]	FUZZY SYSTEM ; UNIVERSAL APPROXIMATION
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000810338500001
出版者	WILEY
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49609
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Chen, Dewang
作者单位	1.Fuzhou Univ, Coll Comp & Data Sci, Fuzhou, Peoples R China 2.FuJian Univ Technol, Sch Transportat, Fuzhou 350118, Peoples R China 3.Fujian Univ Technol, Intelligent Transportat Syst Res Ctr, Fuzhou, Peoples R China 4.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China 5.Fujian Prov Key Lab Network Comp & Intelligent In, Fuzhou, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Huang, Yunhu,Chen, Dewang,Zhao, Wendi,et al. Deep patch learning algorithms with high interpretability for regression problems[J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,2022:38.
APA	Huang, Yunhu,Chen, Dewang,Zhao, Wendi,Lv, Yisheng,&Wang, Shiping.(2022).Deep patch learning algorithms with high interpretability for regression problems.INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,38.
MLA	Huang, Yunhu,et al."Deep patch learning algorithms with high interpretability for regression problems".INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2022):38.