Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis
Autism is a neurodevelopmental disorder with great uncertainty in its diagnosis. However, the existing modeling methods for autism diagnosis have not been effectively studied for the uncertainty of the diagnosis process so far. In this paper, based on TSK (Takagi-Sugeno-Kang) fuzzy system and combin...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2020-12-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2488.shtml |
| _version_ | 1818655067678441472 |
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| author | ZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, DENG Zhaohong, PAN Xiang, WANG Shitong |
| author_facet | ZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, DENG Zhaohong, PAN Xiang, WANG Shitong |
| author_sort | ZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, DENG Zhaohong, PAN Xiang, WANG Shitong |
| collection | DOAJ |
| description | Autism is a neurodevelopmental disorder with great uncertainty in its diagnosis. However, the existing modeling methods for autism diagnosis have not been effectively studied for the uncertainty of the diagnosis process so far. In this paper, based on TSK (Takagi-Sugeno-Kang) fuzzy system and combining the association information between functional connections, a new sparse modeling method JGSL-TSK (joint-group-sparse-learning Takagi-Sugeno-Kang) for uncertain joint group is proposed and applied to the auxiliary diagnosis of autism. Firstly, the original rs-fMRI (resting-state functional magnetic resonance imaging) data are preprocessed and extracted to obtain the reduced dimension feature matrix. Secondly, based on the TSK fuzzy system framework, the joint sparse regulari-zation term is introduced to the consequent parameter learning process from the correlation between features, so as to guide the joint selection of features within the same rule and between rules. Finally, the alternating optimization method is used to solve the model. Compared with the existing methods, this method has the advantages of strong interpretability and good classification performance. Experimental results show that this method is conducive to the auxiliary diagnosis of autism. |
| first_indexed | 2024-12-17T03:03:48Z |
| format | Article |
| id | doaj.art-349f4fca825b4221babf7f447f3a0ffe |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-17T03:03:48Z |
| publishDate | 2020-12-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-349f4fca825b4221babf7f447f3a0ffe2022-12-21T22:06:00ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182020-12-0114122083209310.3778/j.issn.1673-9418.1908067Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided DiagnosisZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, DENG Zhaohong, PAN Xiang, WANG Shitong01. School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China 2. School of Communication & Information Engineering, Shanghai University, Shanghai 200444, ChinaAutism is a neurodevelopmental disorder with great uncertainty in its diagnosis. However, the existing modeling methods for autism diagnosis have not been effectively studied for the uncertainty of the diagnosis process so far. In this paper, based on TSK (Takagi-Sugeno-Kang) fuzzy system and combining the association information between functional connections, a new sparse modeling method JGSL-TSK (joint-group-sparse-learning Takagi-Sugeno-Kang) for uncertain joint group is proposed and applied to the auxiliary diagnosis of autism. Firstly, the original rs-fMRI (resting-state functional magnetic resonance imaging) data are preprocessed and extracted to obtain the reduced dimension feature matrix. Secondly, based on the TSK fuzzy system framework, the joint sparse regulari-zation term is introduced to the consequent parameter learning process from the correlation between features, so as to guide the joint selection of features within the same rule and between rules. Finally, the alternating optimization method is used to solve the model. Compared with the existing methods, this method has the advantages of strong interpretability and good classification performance. Experimental results show that this method is conducive to the auxiliary diagnosis of autism.http://fcst.ceaj.org/CN/abstract/abstract2488.shtmlautism spectrum disorder (asd)resting-state functional magnetic resonance imaging (rs-fmri)tsk fuzzy systemjoint group sparse |
| spellingShingle | ZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, DENG Zhaohong, PAN Xiang, WANG Shitong Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis Jisuanji kexue yu tansuo autism spectrum disorder (asd) resting-state functional magnetic resonance imaging (rs-fmri) tsk fuzzy system joint group sparse |
| title | Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis |
| title_full | Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis |
| title_fullStr | Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis |
| title_full_unstemmed | Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis |
| title_short | Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis |
| title_sort | novel tsk modeling method with joint group sparse learning for autism aided diagnosis |
| topic | autism spectrum disorder (asd) resting-state functional magnetic resonance imaging (rs-fmri) tsk fuzzy system joint group sparse |
| url | http://fcst.ceaj.org/CN/abstract/abstract2488.shtml |
| work_keys_str_mv | AT zhangchunxiangwangjunzhangjiaxudengzhaohongpanxiangwangshitong noveltskmodelingmethodwithjointgroupsparselearningforautismaideddiagnosis |