[1]ZHANG Xinyun,ZHOU Linjia,CHENG Yuting,et al.Domain adaptive Takagi-Sugeno-Kang fuzzy classifier based on pseudo-label refinement[J].CAAI Transactions on Intelligent Systems,2025,20(3):557-570.[doi:10.11992/tis.202408015]
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Domain adaptive Takagi-Sugeno-Kang fuzzy classifier based on pseudo-label refinement

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 3 Page number: 557-570 Column: 学术论文—机器学习 Public date: 2025-05-05
Title:
Domain adaptive Takagi-Sugeno-Kang fuzzy classifier based on pseudo-label refinement
Author(s):
ZHANG Xinyun1;  ZHOU Linjia1;  CHENG Yuting1;  QIU Chengyu1;  XIE Yuhang1;  CHEN Xiu1;  ZHANG Yuanpeng1; 2
1. Department of Medical Informatics, Nantong University, Nantong 226019, China;
2. Department of Health Science, Technology and Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China
Keywords:
domain adaptation; Takagi-Sugeno-Kang fuzzy classifier; random walking; pseudo-label refinement; fuzzy shared feature space; unsupervised learning; fuzzy rule; transfer learning
CLC:
TP391
DOI:
10.11992/tis.202408015
Abstract:
The Takagi-Sugeno-Kang (TSK) fuzzy classifier (FC) has been widely applied to various fields owing to its excellent classification performance and interpretability. To address the degradation of the generalization performance of this TSK TSK FC caused by the differences in the distributions of the training and test samples, a domain adaptive (DA) pseudo-label refinement (PLR)-based TSK FC (DA-TSK-PLR-FC) is proposed. This classifier leverages the nonlinear and linear mapping capabilities of the antecedent and consequent parts in fuzzy rules to construct a fuzzy shared feature space for source and target domain data. In this fuzzy shared feature space, graph-based random walking and label filtering refinement were applied to enhance the pseudo-label quality in the target domain, thereby enhancing the effectiveness of the domain alignment. Further, extensive experiments using multiple public datasets reveal that the proposed DA-TSK-PLR-FC achieves reliable classification performance and good interpretability.
References:
[1] PAN S J, YANG Qiang. A survey on transfer learning[J]. IEEE transactions on knowledge and data engineering, 2009, 22(10): 1345-1359.
[2] AJITH A, GOPAKUMAR G. Domain adaptation: a survey[M]//Computer Vision and Machine Intelligence. Singapore: Springer Nature Singapore, 2023: 591-602.
[3] 李晶晶, 孟利超, 张可, 等. 领域自适应研究综述[J]. 计算机工程, 2021, 47(6): 1-13.
LI Jingjing, MENG Lichao, ZHANG Ke, et al. Review of studies on domain adaptation[J]. Computer engineering, 2021, 47(6): 1-13.
[4] KOUW W M, LOOG M. A review of domain adaptation without target labels[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 43(3): 766-785.
[5] GOPALAN R, LI Ruonan, CHELLAPPA R. Domain adaptation for object recognition: an unsupervised approach[C]//2011 International Conference on Computer Vision. Barcelona: IEEE, 2011: 999-1006.
[6] GONG Boqing, SHI Yuan, SHA Fei, et al. Geodesic flow kernel for unsupervised domain adaptation[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence: USA. IEEE, 2012: 2066-2073.
[7] FERNANDO B, HABRARD A, SEBBAN M, et al. Unsupervised visual domain adaptation using subspace alignment[C]//2013 IEEE International Conference on Computer Vision. Sydney: IEEE, 2013: 2960-2967.
[8] PAN S J, TSANG I W, KWOK J T, et al. Domain adaptation via transfer component analysis[J]. IEEE transactions on neural networks, 2011, 22(2): 199-210.
[9] LONG Mingsheng, WANG Jianming, DING Guiguang, et al. Transfer feature learning with joint distribution adaptation[C]//2013 IEEE International Conference on Computer Vision. Sydney: IEEE, 2013: 2200-2207.
[10] TAKAGI T, SUGENO M. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE transactions on systems, man, and cybernetics: systems, 1985, SMC-15(1): 116-132.
[11] YANG Changjian, DENG Zhaohong, CHOI K S, et al. Takagi–Sugeno–Kang transfer learning fuzzy logic system for the adaptive recognition of epileptic electroencephalogram signals[J]. IEEE transactions on fuzzy systems, 2015, 24(5): 1079-1094.
[12] FENG Hao, PENG Yaxin, ZHANG Guixu, et al. Joint distribution adaptation based TSK fuzzy logic system for epileptic EEG signal identification[C]//2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Shenzhen: IEEE, 2016: 340-345.
[13] XU Peng, DENG Zhaohong, WANG Jun, et al. Transfer representation learning with TSK fuzzy system[J]. IEEE transactions on fuzzy systems, 2019, 29(3): 649-663.
[14] WANG Wei, LI Baopu, WANG Mengzhu, et al. Confidence regularized label propagation based domain adaptation[J]. IEEE transactions on circuits and systems for video technology, 2021, 32(6): 3319-3333.
[15] ZHANG Yuanpeng, WANG Guanjin, ZHOU Ta, et al. Takagi-Sugeno-Kang fuzzy system fusion: a survey at hierarchical, wide and stacked levels[J]. Information fusion, 2024, 101: 101977.
[16] CHUANG C C, HSIAO C C, JENG J T. Adaptive fuzzy regression clustering algorithm for TSK fuzzy modeling[C]//Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium. Kobe: IEEE, 2003: 201-206.
[17] ZHU Lin, CHUNG F L, WANG Shitong. Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions[J]. IEEE transactions on systems, man, and cybernetics, part B (cybernetics), 2009, 39(3): 578-591.
[18] TIAN Qing, SUN Heyang, PENG Shun, et al. Self-adaptive label filtering learning for unsupervised domain adaptation[J]. Frontiers of computer science, 2022, 17(1): 171308.
[19] BLITZER J, CRAMMER K, KULESZA A, et al. Learning bounds for domain adaptation[C]//Proceedings of the 21st International Conference on Neural Information Processing Systems. Vancouver: ACM, 2007: 129-136.
[20] SAENKO K, KULIS B, FRITZ M, et al. Adapting visual category models to new domains[M]//Computer Vision – ECCV 2010. Berlin: Springer Berlin Heidelberg, 2010: 213-226.
[21] GRIFFIN G, HOLUB A, PERONA P. Caltech-256 object category dataset[R]. California Institute of Technology, 2007.
[22] SIM T, BAKER S, BSAT M. The CMU pose, illumination, and expression (PIE) database[C]//Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition. Washington, DC: IEEE, 2002: 53-58.
[23] BLITZER J, DREDZE M, PEREIRA F. Biographies bollywood boom-boxes and blenders: Domain adaptation for sentiment classification[C]//Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague: Association for Computational Linguistics, 2007, 7: 440-447.
[24] NOZZA D, FERSINI E, MESSINA E. Deep learning and ensemble methods for domain adaptation[C]//2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). San Jose: IEEE, 2016: 184-189.
[25] LONG Mingsheng, WANG Jianmin, DING Guiguang, et al. Transfer joint matching for unsupervised domain adaptation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 1410-1417.
[26] SUN Baochen, FENG Jiashi, SAENKO K. Return of frustratingly easy domain adaptation[J]. Proceedings of the AAAI conference on artificial intelligence, 2016, 30(1): 2058-2065.
[27] ZHANG Jing, LI Wanqing, OGUNBONA P. Joint geometrical and statistical alignment for visual domain adaptation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 5150-5158.
[28] ZHANG Wen, WU Dongrui. Discriminative joint probability maximum mean discrepancy (DJP-MMD) for domain adaptation[C]//2020 International Joint Conference on Neural Networks. Glasgow: IEEE, 2020: 1-8.
[29] WANG Jie, ZHANG Xiaolei. Improving pseudo labels with intra-class similarity for unsupervised domain adaptation[J]. Pattern recognition, 2023, 138: 109379.
[30] QIN Bin, NOJIMA Y, ISHIBUCHI H, et al. Realizing deep high-order TSK fuzzy classifier by ensembling interpretable zero-order TSK fuzzy subclassifiers[J]. IEEE transactions on fuzzy systems, 2021, 29(11): 3441-3455.
[31] 丁卫平, 顾卫江, 董建成, 等. 模糊逻辑推理在电子病历智能辅助诊断系统中的应用研究[J]. 南通大学学报(自然科学版), 2006, 5(4): 77-81.
DING Weiping, GU Weijiang, DONG Jiancheng, et al. Application of fuzzy logic reasoning in intelligent assistant diagnosis system of electronic patient record[J]. Journal of Nantong University (natural science), 2006, 5(4): 77-81.
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