Deep patch learning algorithms with high interpretability for regression problems
Huang, Yunhu1,2; Chen, Dewang2,3,4; Zhao, Wendi2,3; Lv, Yisheng4; Wang, Shiping1,5
Source PublicationINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
ISSN0884-8173
2022-06-14
Pages38
Corresponding AuthorChen, Dewang(dwchen@fjut.edu.cn)
AbstractImproving 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.
Keyworddeep learning deep patch learning fuzzy system fuzzy C-means clustering interpretability maximum information coefficient (MIC) Pearson's correlation coefficients (PCC)
DOI10.1002/int.22937
WOS KeywordFUZZY SYSTEM ; UNIVERSAL APPROXIMATION
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
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000810338500001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49609
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, 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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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