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Deep patch learning algorithms with high interpretability for regression problems

文献类型：期刊论文

作者Huang, Yunhu4,5; Chen, Dewang2,3,4; Zhao, Wendi3,4; Lv, Yisheng2; Wang, Shiping1,5
刊名INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
出版日期2022-06-14
页码38
关键词deep learning deep patch learning fuzzy system fuzzy C-means clustering interpretability maximum information coefficient (MIC) Pearson's correlation coefficients (PCC)
ISSN号0884-8173
DOI10.1002/int.22937
通讯作者Chen, Dewang(dwchen@fjut.edu.cn)
英文摘要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.
WOS关键词FUZZY SYSTEM ; UNIVERSAL APPROXIMATION
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研究方向Computer Science
语种英语
WOS记录号WOS:000810338500001
出版者WILEY
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
源URL[http://ir.ia.ac.cn/handle/173211/49608]  
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.Fujian Prov Key Lab Network Comp & Intelligent In, Fuzhou, Peoples R China
2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China
3.Fujian Univ Technol, Intelligent Transportat Syst Res Ctr, Fuzhou, Peoples R China
4.FuJian Univ Technol, Sch Transportat, Fuzhou 350118, Peoples R China
5.Fuzhou Univ, Coll Comp & Data Sci, Fuzhou, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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