A comprehensive survey of data classification based on evidence theory

Abstract

Data classification is a fundamental task in machine learning and data analysis, with applications across many fields. In practice, uncertainty often arises due to weakly discriminative features, missing values, distribution shift, class imbalance, inconsistent label spaces, and class overlap. Traditional classification methods, which typically rely on probabilistic frameworks, may not explicitly represent or reduce such uncertainty. Evidence theory (ET), also known as Dempster-Shafer theory, provides a flexible framework for modeling uncertainty and imprecision through basic belief assignments and the evidence combination rule, and has attracted growing attention in the data classification field. Consequently, data classification based on ET (DCET) has become an active research topic. This paper provides a comprehensive survey of DCET, systematically categorizing DCET methods by feature- and label-based uncertainty scenarios. We first review ET fundamentals and summarize the evidential K-nearest-neighbor classifier and its variants. We then survey ET-based methods for tabular data classification under six major uncertainty scenarios: high-dimensional features, missing attribute values, feature distribution shift, imbalanced label distribution, inconsistent label spaces, and class overlap. Next, we review ET-based methods for image (unstructured) classification, highlighting the integration of ET with deep neural networks. We further summarize representative applications, including human activity recognition, medical image segmentation, remote sensing image classification, and remote sensing image change detection. Finally, we discuss future research directions for DCET.

Keywords

Evidence theory / Uncertain data classification / Belief functions / Evidential reasoning / Dempster–Shafer theory / Multi-source information fusion

References

[1] Abdar M , Pourpanah F , Hussain S , et al., 2021. A review of uncertainty quantification in deep learning:techniques, applications and challenges. Inform Fus, 76: 243- 297.

[2] Ahmad F , Xiong J , Wu J , et al., 2025. Cardiac dynamic characteristics classification on cine MRI using semi-supervised imaging approach. Mach Intell Res, 22 (6): 1102- 1115.

[3] Alcala-Fdez J , Alcala R , Herrera F , 2011. A fuzzy association rulebased classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans Fuzzy Syst, 19 (5): 857- 872.

[4] Amini A , Schwarting W , Soleimany A , et al., 2020. Deep evidential regression. Proc 34^th Int Conf on Neural Information Processing Systems, Article 1251.

[5] Attoh-Okine NO , 2002. Aggregating evidence in pavement management decision-making using belief functions and qualitative Markov tree. IEEE Trans Syst Man Cybern Part C (Appl Rev), 32 (3): 243- 251.

[6] Campagner A , Ciucci D , Denœux T , 2022. Belief functions and rough sets:survey and new insights. Int J Approx Reason, 143: 192- 215.

[7] Cao Y , Zhou ZJ , Hu CH , et al., 2021. On the interpretability of belief rule-based expert systems. IEEE Trans Fuzzy Syst, 29 (11): 3489- 3503.

[8] Chang LL , Zhou ZJ , Liao HC , et al., 2019. Generic disjunctive beliefrule-base modeling, inferencing, and optimization. IEEE Trans Fuzzy Syst, 27 (9): 1866- 1880.

[9] Chen L , Lou YH , He JZ , et al., 2022. Evidential neighborhood contrastive learning for universal domain adaptation. Proc 36^th AAAI Conf on Artificial Intelligence, p.6258- 6267.

[10] Chen M , Goel K , Sohoni NS , et al., 2021. Mandoline:model evaluation under distribution shift. Proc 38^th Int Conf on Machine Learning, p.1617- 1629.

[11] Chen ZC , Cai R , 2022. Updating incomplete framework of target recognition database based on fuzzy gap statistic. Eng Appl Artif Intell, 107: 104521.

[12] Cui HZ , Chang YH , Zhang HQ , et al., 2023. Determine the number of unknown targets in the open world from the perspective of bidirectional analysis using gap statistic and isolation forest. Inform Sci, 623: 832- 856.

[13] Dai YW , Zhu HQ , Yang SY , et al., 2022. GCL-OSDA:uncertainty prediction-based graph collaborative learning for open-set domain adaptation. Knowl-Based Syst, 256: 109850.

[14] Deb R , Liew AWC , 2016. Missing value imputation for the analysis of incomplete traffic accident data. Inform Sci, 339: 274- 289.

[15] Dempster AP , 1967. Upper and lower probabilities induced by a multivalued mapping. Ann Math Statist, 38 (2): 325- 339.

[16] Deng Z , Wang JY , 2021. A new evidential similarity measurement based on Tanimoto measure and its application in multi-sensor data fusion. Eng Appl Artif Intell, 104: 104380.

[17] Denœux T , 1995. A K-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans Syst Man Cybern, 25 (5): 804- 813.

[18] Denœux T , 1997. Analysis of evidence-theoretic decision rules for pattern classification. Patt Recogn, 30 (7): 1095- 1107.

[19] Denœux T , 2000. A neural network classifier based on Dempster-Shafer theory. IEEE Trans Syst Man Cybern Part A Syst Humans, 30 (2): 131- 150.

[20] Denœux T , 2008. Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artif Intell, 172 (2-3): 234- 264.

[21] Denœux T , 2016. 40 years of Dempster-Shafer theory. Int J Approx Reason, 79 (C): 1- 6.

[22] Denœux T , 2019a. Decision-making with belief functions:a review. Int J Approx Reason, 109: 87- 110.

[23] Denœux T , 2019b. Logistic regression, neural networks and Dempster-Shafer theory:a new perspective. Knowl-Based Syst, 176: 54- 67.

[24] Denœux T , 2021. Distributed combination of belief functions. Inform Fus, 65: 179- 191.

[25] Denœux T , 2025. Uncertainty measures in a generalized theory of evidence. Fuzzy Sets Syst, 520: 109546.

[26] Denœux T , Masson MH , 2004. EVCLUS:evidential clustering of proximity data. IEEE Trans Syst Man Cybern Part B (Cybern), 34 (1): 95- 109.

[27] Denœux T , Shenoy PP , 2020. An interval-valued utility theory for decision making with Dempster-Shafer belief functions. Int J Approx Reason, 124: 194- 216.

[28] Denœux T , Smets P , 2006. Classification using belief functions:relationship between case-based and model-based approaches. IEEE Trans Syst Man Cybern Part B (Cybern), 36 (6): 1395- 1406.

[29] Denœux T , Kanjanatarakul O , Sriboonchitta S , 2019. A new evidential K-nearest neighbor rule based on contextual discounting with partially supervised learning. Int J Approx Reason, 113: 287- 302.

[30] Dong GG , Kuang GY , 2015. Target recognition via information aggregation through Dempster-Shafer's evidence theory. IEEE Geosci Remote Sens Lett, 12 (6): 1247- 1251.

[31] Dong YL , Zhou Y , 2023. Hierarchical belief K-nearest neighbors for human activity recognition. Proc 42^nd Chinese Control Conf, p.3109- 3114.

[32] Dong YL , Li XD , Dezert J , et al., 2020. Dezert-Smarandache theorybased fusion for human activity recognition in body sensor networks. IEEE Trans Ind Inform, 16 (11): 7138- 7149.

[33] Dong YL , Li XD , Dezert J , et al., 2021a. Evidential reasoning with hesitant fuzzy belief structures for human activity recognition. IEEE Trans Fuzzy Syst, 29 (12): 3607- 3619.

[34] Dong YL , Li XD , Dezert J , et al., 2021b. Multi-criteria analysis of sensor reliability for wearable human activity recognition. IEEE Sens J, 21 (17): 19144- 19156.

[35] Dong YL , Zhou RG , Zhu CM , et al., 2022. Hierarchical activity recognition based on belief functions theory in body sensor networks. IEEE Sens J, 22 (15): 15211- 15221.

[36] Dong YL , Li XD , Dezert J , et al., 2023a. Multisource weighted domain adaptation with evidential reasoning for activity recognition. IEEE Trans Ind Inform, 19 (4): 5530- 5542.

[37] Dong YL , Xu KH , Zhu CM , et al., 2023b. E-FPN:evidential feature pyramid network for ship classification. Remote Sens, 15 (15): 3916.

[38] Dong YL , Li XD , Dezert J , et al., 2024. Graph-structure-based multigranular belief fusion for human activity recognition. IEEE Trans Neur Netw Learn Syst, 35 (10): 13589- 13603.

[39] Dong YL , Shi ZL , Li XD , et al., 2025. Multiview uncertainty-aware fusion for human activity recognition via Dempster-Shafer theory. IEEE Trans Ind Inform, 21 (4): 3037- 3046.

[40] Du PJ , Liu SC , Gamba P , et al., 2012. Fusion of difference images for change detection over urban areas. IEEE J Sel Top Appl Earth Obs Remote Sens, 5 (4): 1076- 1086.

[41] Du PJ , Liu SC , Xia JS , et al., 2013. Information fusion techniques for change detection from multi-temporal remote sensing images. Inform Fus, 14 (1): 19- 27.

[42] Dubois D , Prade H , 1988. Representation and combination of uncertainty with belief functions and possibility measures. Comput Intell, 4 (3): 244- 264.

[43] Fan D , Yu Y , Wang ZJ , et al., 2022. Partial label learning based on disambiguation correction net with graph representation. IEEE Trans Circ Syst Video Technol, 32 (8): 4953- 4967.

[44] Fan JF , Yu Y , Huang LQ , et al., 2023. GraphDPI:partial label disambiguation by graph representation learning via mutual information maximization. Patt Recogn, 134: 109133.

[45] Fan JF , Huang LQ , Gong CY , et al., 2025. KMT-PLL:K-means crossattention Transformer for partial label learning. IEEE Trans Neur Netw Learn Syst, 36 (2): 2789- 2800.

[46] Fan JF , Li J , Zhong X , et al., 2026. Evidential deep partial label learning to quantify disambiguation uncertainty. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.24770- 24779.

[47] Fei LG , Lu JD , Feng YQ , 2020. An extended best-worst multi-criteria decision-making method by belief functions and its applications in hospital service evaluation. Comput Ind Eng, 142: 106355.

[48] Feng J , Huang LQ , Li JN , et al., 2026. Partial label learning via mutual information representation learning. IEEE Trans Circ Syst Video Technol, 36 (3): 3709- 3723.

[49] Feng YB , Yi X , Wang SH , et al., 2025. HyperEDL:spectral-spatial evidence deep learning for cross-scene hyperspectral image classification. IEEE Trans Geosci Remote Sens, 63: 5508316.

[50] Feng ZC , Zhou ZJ , Hu CH , et al., 2019. A new belief rule base model with attribute reliability. IEEE Trans Fuzzy Syst, 27 (5): 903- 916.

[51] Feng ZC , He W , Zhou ZJ , et al., 2021. A new safety assessment method based on belief rule base with attribute reliability. IEEE/CAA J Autom Sin, 8 (11): 1774- 1785.

[52] Frikha A , Moalla H , 2015. Analytic hierarchy process for multi-sensor data fusion based on belief function theory. Eur J Oper Res, 241 (1): 133- 147.

[53] Fu C , Zhan QS , Liu WY , 2021. Evidential reasoning based ensemble classifier for uncertain imbalanced data. Inform Sci, 578: 378- 400.

[54] Fu YG , Ye JF , Yin ZF , et al., 2021. Construction of EBRB classifier for imbalanced data based on fuzzy c-means clustering. Knowl-Based Syst, 234: 107590.

[55] Fu YM , Liu ZG , Zhang ZW , 2023. Progressive learning vision Transformer for open set recognition of fine-grained objects in remote sensing images. IEEE Trans Geosci Remote Sens, 61: 5215113.

[56] Fu YM , Liu ZG , Wang ZC , 2024. Logit prototype learning with active multimodal representation for robust open-set recognition. Sci China Inform Sci, 67 (6): 162204.

[57] Gao JY , Chen MY , Xiang LY , et al., 2026. A comprehensive survey on evidential deep learning and its applications. IEEE Trans Patt Anal Mach Intell, 48 (3): 2118- 2138.

[58] Gao M , Hong X , Harris CJ , 2014. Construction of neurofuzzy models for imbalanced data classification. IEEE Trans Fuzzy Syst, 22 (6): 1472- 1488.

[59] Geng CX , Huang SJ , Chen SC , 2021. Recent advances in open set recognition:a survey. IEEE Trans Patt Anal Mach Intell, 43 (10): 3614- 3631.

[60] Geng XJ , Liang Y , Jiao LM , 2020. Multi-frame decision fusion based on evidential association rule mining for target identification. Appl Soft Comput, 94: 106460.

[61] Geng XJ , Liang Y , Jiao LM , 2021. Evidential association classification for high-dimensional data. Proc IEEE Int Conf on Cloud Computing and Big Data Analytics, p.100- 105.

[62] Geng XJ , Chen XH , Ma ZF , et al., 2024. Multi-framework evidential association rule fusion for wearable human activity recognition. IEEE Sens J, 24 (7): 11805- 11816.

[63] Gil-Gonzalez J , Orozco-Gutierrez A , Alvarez-Meza A , 2021. Learning from multiple inconsistent and dependent annotators to support classification tasks. Neurocomputing, 423: 236- 247.

[64] Gong CY , Su ZG , Wang PH , et al., 2020a. Cumulative belief peaks evidential K-nearest neighbor clustering. Knowl-Based Syst, 200: 105982.

[65] Gong CY , Wang PH , Su ZG , 2020b. An interactive nonparametric evidential regression algorithm with instance selection. Soft Comput, 24 (5): 3125- 3140.

[66] Gong CY , Su ZG , Wang PH , et al., 2021a. Evidential instance selection for K-nearest neighbor classification of big data. Int J Approx Reason, 138: 123- 144.

[67] Gong CY , Su ZG , Wang PH , et al., 2021b. An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods. Patt Recogn, 113: 107751.

[68] Gong CY , Su ZG , Wang Q , et al., 2022a. Distributed EK-NN classification. Proc 7^th Int Conf on Belief Functions:Theory and Applications, p.99- 108.

[69] Gong CY , Su ZG , Wang PH , et al., 2022b. Distributed evidential clustering toward time series with big data issue. Expert Syst Appl, 191: 116279.

[70] Gong CY , Li YB , Liu Y , et al., 2022c. Joint evidential K-nearest neighbor classification. Proc IEEE 38^th Int Conf on Data Engineering, p.2113- 2126.

[71] Gong CY , Su ZG , Wang PH , et al., 2023. A sparse reconstructive evidential K-nearest neighbor classifier for high-dimensional data. IEEE Trans Knowl Data Eng, 35 (6): 5563- 5576.

[72] Gong CY , Demmel J , You Y , 2024. Scalable evidential K-nearest neighbor classification on big data. IEEE Trans Big Data, 10 (3): 226- 237.

[73] Gong CY , Su ZG , Denœux T , 2025. Multi-view evidential K-NN classification. Inform Fus, 120: 103113.

[74] Grina F , Elouedi Z , Lefevre E , 2022. Learning from imbalanced data using an evidential undersampling-based ensemble. Proc 15^th Int Conf on Scalable Uncertainty Management, p.235- 248.

[75] Grina F , Elouedi Z , Lefevre E , 2023. Re-sampling of multi-class imbalanced data using belief function theory and ensemble learning. Int J Approx Reason, 156: 1- 15.

[76] Gu TX , Huang LQ , Liu GS , 2025. GTRNet:graph topology-aware refinement network for user role classification in social networks. Proc IEEE Int Conf on Systems, Man, and Cybernetics, p.3307- 3312.

[77] Guan H , Song CF , Zhang ZX , 2025. Lidar-camera cooperative semantic segmentation. Mach Intell Res, 22 (5): 956- 968.

[78] Hadi W , Al-Radaideh QA , Alhawari S , 2018. Integrating associative rule-based classification with Naive Bayes for text classification. Appl Soft Comput, 69: 344- 356.

[79] Han ZB , Zhang CQ , Fu HZ , et al., 2021. Trusted multi-view classification. Proc 9^th Int Conf on Learning Representations, p.1- 11.

[80] Han ZB , Yang F , Huang JZ , et al., 2022. Multimodal dynamics:dynamical fusion for trustworthy multimodal classification. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.20675- 20685.

[81] Han ZB , Zhang CQ , Fu HZ , et al., 2023. Trusted multi-view classification with dynamic evidential fusion. IEEE Trans Patt Anal Mach Intell, 45 (2): 2551- 2566.

[82] He HF , Liao RB , Li YT , 2025. MSAFNet:a novel approach to facial expression recognition in embodied AI systems. Intell Robot, 5 (2): 313- 332.

[83] He WC , Jiang Z , Xiao TS , et al., 2026. A survey on uncertainty quantification methods for deep learning. ACM Comput Surv, 58 (7): 179.

[84] He YL , Ye X , Huang JZ , et al., 2022. Bayesian attribute bagging-based extreme learning machine for high-dimensional classification and regression. ACM Trans Intell Syst Technol, 13 (2): 1- 26.

[85] Hu JW , Zheng BY , Wang C , et al., 2020. A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments. Front Inform Technol Electron Eng, 21 (5): 675- 692.

[86] Hu ZT , Su YJ , Hou W , et al., 2023. Multi-sensor data fusion method based on divergence measure and probability transformation belief factor. Appl Soft Comput, 145: 110603.

[87] Huang L , Denœux T , Tonnelet D , et al., 2021a. Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation. Proc 12^th Int Workshop on Machine Learning in Medical Imaging, p.30- 39.

[88] Huang L , Ruan S , Decazes P , et al., 2021b. Evidential segmentation of 3D PET/CT images. Proc 6^th Int Conf on Belief Functions:Theory and Applications, p.159- 167.

[89] Huang L , Denœux T , Vera P , et al., 2022a. Evidence fusion with contextual discounting for multi-modality medical image segmentation. Proc 25^th Int Conf on Medical Image Computing and Computer Assisted Intervention, p.401- 411.

[90] Huang L , Ruan S , Decazes P , et al., 2022b. Lymphoma segmentation from 3D PET-CT images using a deep evidential network. Int J Approx Reason, 149: 39- 60.

[91] Huang L , Ruan S , Denœux T , 2023a. Application of belief functions to medical image segmentation:a review. Inform Fus, 91: 737- 756.

[92] Huang L , Liu ZG , Dezert J , 2023b. Cross-domain pattern classification with distribution adaptation based on evidence theory. IEEE Trans Cybern, 53 (2): 718- 731.

[93] Huang L , Ruan S , Denœux T , 2023c. Semi-supervised multiple evidence fusion for brain tumor segmentation. Neurocomputing, 535: 40- 52.

[94] Huang LQ , 2024. Combination of information in labeled and unlabeled data via evidence theory. IEEE Trans Artif Intell, 5 (5): 2179- 2192.

[95] Huang LQ , Liu GS , 2024. Convolutional feature aggregation network with self-supervised learning and decision fusion for SAR target recognition. IEEE Trans Instrum Meas, 73: 2526714.

[96] Huang LQ , Liu GS , 2026. Dual joint covariance alignment method for incomplete data classification. IEEE Trans Neur Netw Learn Syst, early access.

[97] Huang LQ , Liu ZG , Pan Q , et al., 2020. Evidential combination of augmented multi-source of information based on domain adaptation. Sci China Inform Sci, 63 (11): 210203.

[98] Huang LQ , Fan JF , Zhao WB , et al., 2023a. A new multi-source transfer learning method based on two-stage weighted fusion. KnowlBased Syst, 262: 110233.

[99] Huang LQ , Zhao WB , Liew AWC , et al., 2023b. An evidential combination method with multi-color spaces for remote sensing image scene classification. Inform Fus, 93: 209- 226.

[100] Huang LQ , Fan JF , Xu K , 2024a. SAR target recognition based on convolutional feature aggregation and decision combination. Proc IEEE Int Geoscience and Remote Sensing Symp, p.11008- 11011.

[101] Huang LQ , Zhao WB , Liu Y , et al., 2024b. An evidential multi-target domain adaptation method based on weighted fusion for crossdomain pattern classification. IEEE Trans Neur Netw Learn Syst, 35 (10): 14218- 14232.

[102] Huang LQ , Chen BG , Hu YM , et al., 2025a. Handcrafted and deep feature aggregation network for identification of real and fake satellite image with limited data. Proc IEEE Int Geoscience and Remote Sensing Symp, p.6606- 6609.

[103] Huang LQ , Fan JF , Liew AWC , 2025b. Integration of multikinds imputation with covariance adaptation based on evidence theory. IEEE Trans Neur Netw Learn Syst, 36 (5): 8657- 8671.

[104] Huang LQ , Fan JF , Wang SL , et al., 2025c. Multi-source multitarget domain adaptation based on evidence theory. IEEE Trans Instrum Meas, 74: 2524317.

[105] Huang LQ , Fan JF , Wang SL , et al., 2025d. A new multi-target domain adaptation method based on evidence theory for distribution inconsistent data classification. IEEE Trans Syst Man Cybern Syst, 55 (6): 4125- 4139.

[106] Huang LQ , Fan JF , Wang SL , et al., 2025e. Towards a distribution alignment framework for incomplete data classification. Proc IEEE Int Conf on Acoustics, Speech and Signal Processing, p.1- 5.

[107] Huang LQ , Zhu J , Hu YM , et al., 2025f. New multi-source distributed transfer learning framework. Proc IEEE Int Conf on Image Processing, p.241- 246.

[108] Huang LQ , Fan JF , Liu GS , et al., 2026a. A robust multi-source transfer classification method based on belief functions for crossdomain pattern recognition. Int J Approx Reason, 189: 109589.

[109] Huang LQ , Fan JF , Wang SL , et al., 2026b. Incomplete data classification via distribution alignment with evidence combination. Mach Intell Res, 23 (3): 593- 615.

[110] Huang M , Zhang C , 2022. A novel multi-source domain adaptation method with Dempster-Shafer evidence theory for cross-domain classification. Mathematics, 10 (15): 2797.

[111] Huang T , Liu ZK , Wang R , et al., 2025. Visual hallucination detection in large vision-language models via evidential conflict. Int J Approx Reason, 186: 109507.

[112] Ji FC , Zhao WZ , Wang Q , et al., 2024. Spectral-spatial evidential learning network for open-set hyperspectral image classification. IEEE Trans Geosci Remote Sens, 62: 5503617.

[113] Jiang W , Chang Y , Wang SY , et al., 2017. A method to identify the incomplete framework of discernment in evidence theory. Math Probl Eng, 2017: 7635972.

[114] Jiao LM , Pan Q , Denœux T , et al., 2015a. Belief rule-based classification system:extension of FRBCS in belief functions framework. Inform Sci, 309: 26- 49.

[115] Jiao LM , Pan Q , Feng XX , 2015b. Multi-hypothesis nearest-neighbor classifier based on class-conditional weighted distance metric. Neurocomputing, 151: 1468- 1476.

[116] Jiao LM , Denœux T , Pan Q , 2016. A hybrid belief rule-based classification system based on uncertain training data and expert knowledge. IEEE Trans Syst Man Cybern Syst, 46 (12): 1711- 1723.

[117] Jiao LM , Geng XJ , Pan Q , 2019. EEkNN:k-nearest neighbor classifier with an evidential editing procedure for training samples. Electronics, 8 (5): 592.

[118] Jiao LM , Ma XG , Zhang H , et al., 2026a. Dynamic belief rule learning for classification with expert in the loop. IEEE Trans Artif Intell, 7 (4): 2142- 2156.

[119] Jiao LM , Xiao XQ , Gou SJ , et al., 2026b. An evidential deep neural network for set-valued classification and novelty detection. IEEE Trans Artif Intell, 7 (7): 4161- 4175.

[120] Jiao LM , Xiao XQ , Gou SJ , 2026c. MBHF-net:a multi-source belief hybrid fusion network for trusted target recognition. Neurocomputing, 669: 132462.

[121] Ju HR , Ding WP , Shi ZQ , et al., 2022. Attribute reduction with personalized information granularity of nearest mutual neighbors. Inform Sci, 613: 114- 138.

[122] Ju HR , Lu Y , Ding WP , et al., 2024. Three-way evidence theory-based density peak clustering with the principle of justifiable granularity. Appl Soft Comput, 152: 111217.

[123] Kadir ME , Akash PS , Sharmin S , et al., 2020. A proximity weighted evidential k nearest neighbor classifier for imbalanced data. Proc 24^th Pacific-Asia Conf on Advances in Knowledge Discovery and Data Mining, p.71- 83.

[124] Keller JM , Gray MR , Givens JA , 1985. A fuzzy K-nearest neighbor algorithm. IEEE Trans Syst Man Cybern, SMC-15 (4): 580- 585.

[125] Koziarski M , 2020. Radial-based undersampling for imbalanced data classification. Patt Recogn, 102: 107262.

[126] Laanaya H , Martin A , Aboutajdine D , et al., 2010. Support vector regression of membership functions and belief functions-application for pattern recognition. Inform Fus, 11 (4): 338- 350.

[127] Laghmara H , Laurain T , Cudel C , et al., 2020. Heterogeneous sensor data fusion for multiple object association using belief functions. Inform Fus, 57: 44- 58.

[128] Laha A , Pal NR , Das J , 2006. Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory. IEEE Trans Geosci Remote Sens, 44 (6): 1633- 1641.

[129] Le Hégarat-Mascle S , Seltz R , 2004. Automatic change detection by evidential fusion of change indices. Remote Sens Environ, 91 (3-4): 390- 404.

[130] Le Hegarat-Mascle S , Bloch I , Vidal-Madjar D , 1997. Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans Geosci Remote Sens, 35 (4): 1018- 1031.

[131] Lee H , Kwon H , 2021. DBF:dynamic belief fusion for combining multiple object detectors. IEEE Trans Patt Anal Mach Intell, 43 (5): 1499- 1514.

[132] Lee H , Kwon H , Robinson RM , et al., 2016. Dynamic belief fusion for object detection. Proc IEEE Winter Conf on Applications of Computer Vision, p.1- 9.

[133] Lelandais B , Ruan S , Denœux T , et al., 2014. Fusion of multi-tracer PET images for dose painting. Med Image Anal, 18 (7): 1247- 1259.

[134] Li BL , Han ZB , Li HN , et al., 2022. Trustworthy long-tailed classification. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.6960- 6969.

[135] Li XD , Dunkin F , Dezert J , 2024. Multi-source information fusion:progress and future. Chin J Aeronaut, 37 (7): 24- 58.

[136] Li ZY , Huang LQ , Gu TH , et al., 2025. Partial multi-label learning via K-means graph Transformer. Knowl-Based Syst, 325: 114017.

[137] Lian CF , Ruan S , Denœux T , 2015. An evidential classifier based on feature selection and two-step classification strategy. Patt Recogn, 48 (7): 2318- 2327.

[138] Lian CF , Ruan S , Denœux T , et al., 2016a. Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Med Image Anal, 32: 257- 268.

[139] Lian CF , Ruan S , Denœux T , 2016b. Dissimilarity metric learning in the belief function framework. IEEE Trans Fuzzy Syst, 24 (6): 1555- 1564.

[140] Lian CF , Ruan S , Denœux T , et al., 2018. Spatial evidential clustering with adaptive distance metric for tumor segmentation in FDGPET images. IEEE Trans Biomed Eng, 65 (1): 21- 30.

[141] Lian CF , Ruan S , Denœux T , et al., 2019. Joint tumor segmentation in PET-CT images using co-clustering and fusion based on belief functions. IEEE Trans Image Process, 28 (2): 755- 766.

[142] Liew AWC , Law NF , Yan H , 2011. Missing value imputation for gene expression data:computational techniques to recover missing data from available information. Brief Bioinform, 12 (5): 498- 513.

[143] Lin CJ , Leony F , 2024. Evidence-based adaptive oversampling algorithm for imbalanced classification. Knowl Inform Syst, 66 (3): 2209- 2233.

[144] Liu C , Su ZG , Zhao G , 2022. Evidential regression by synthesizing feature selection and parameters learning. Proc 7^th Int Conf on Belief Functions:Theory and Applications, p.87- 95.

[145] Liu D , Zhang YR , Ren Y , et al., 2025. Multi-directional attention:a lightweight attention module for slender structures. Intell Robot, 5 (4): 827- 843.

[146] Liu F , Deng Y , 2021. Determine the number of unknown targets in open world based on elbow method. IEEE Trans Fuzzy Syst, 29 (5): 986- 995.

[147] Liu W , Yue XD , Chen YF , et al., 2022. Trusted multi-view deep learning with opinion aggregation. Proc 36^th AAAI Conf on Artificial Intelligence, p.7585- 7593.

[148] Liu W , Chen YF , Yue XD , et al., 2023. Safe multi-view deep classification. Proc 37^th AAAI Conf on Artificial Intelligence, p.8870- 8878.

[149] Liu YW , Zhang Y , Wang XD , et al., 2025. Evidential K-nearest neighbors with cognitive-inspired feature selection for high-dimensional data. Big Data Cogn Comput, 9 (8): 202.

[150] Liu Z , 2023. An effective conflict management method based on belief similarity measure and entropy for multi-sensor data fusion. Artif Intell Rev, 56 (12): 15495- 15522.

[151] Liu Z , Letchmunan S , 2024. Representing uncertainty and imprecision in machine learning:a survey on belief functions. J King Saud Univ-Comput Inform Sci, 36 (1): 101904.

[152] Liu ZG , Dezert J , Mercier G , et al., 2012. Dynamic evidential reasoning for change detection in remote sensing images. IEEE Trans Geosci Remote Sens, 50 (5): 1955- 1967.

[153] Liu ZG , Pan Q , Dezert J , 2013a. Evidential classifier for imprecise data based on belief functions. Knowl-Based Syst, 52: 246- 257.

[154] Liu ZG , Pan Q , Dezert J , 2013b. A new belief-based K-nearest neighbor classification method. Patt Recogn, 46 (3): 834- 844.

[155] Liu ZG , Pan Q , Dezert J , 2014a. A belief classification rule for imprecise data. Appl Intell, 40: 214- 228.

[156] Liu ZG , Pan Q , Dezert J , et al., 2014b. Credal classification rule for uncertain data based on belief functions. Patt Recogn, 47 (7): 2532- 2541.

[157] Liu ZG , Mercier G , Dezert J , et al., 2014c. Change detection in heterogeneous remote sensing images based on multidimensional evidential reasoning. IEEE Geosci Remote Sens Lett, 11 (1): 168- 172.

[158] Liu ZG , Pan Q , Dezert J , 2014d. Classification of uncertain and imprecise data based on evidence theory. Neurocomputing, 133: 459- 470.

[159] Liu ZG , Liu Y , Dezert J , et al., 2015a. Classification of incomplete data based on belief functions and K-nearest neighbors. Knowl-Based Syst, 89: 113- 125.

[160] Liu ZG , Pan Q , Mercier G , et al., 2015b. A new incomplete pattern classification method based on evidential reasoning. IEEE Trans Cybern, 45 (4): 635- 646.

[161] Liu ZG , Pan Q , Dezert J , et al., 2016. Adaptive imputation of missing values for incomplete pattern classification. Patt Recogn, 52: 85- 95.

[162] Liu ZG , Pan Q , Dezert J , et al., 2017. Hybrid classification system for uncertain data. IEEE Trans Syst Man Cybern Syst, 47 (10): 2783- 2790.

[163] Liu ZG , Li G , Mercier G , et al., 2018. Change detection in heterogenous remote sensing images via homogeneous pixel transformation. IEEE Trans Image Process, 27 (4): 1822- 1834.

[164] Liu ZG , Huang LQ , Zhou K , et al., 2021a. Combination of transferable classification with multisource domain adaptation based on evidential reasoning. IEEE Trans Neur Netw Learn Syst, 32 (5): 2015- 2029.

[165] Liu ZG , Zhang XX , Niu JW , et al., 2021b. Combination of classifiers with different frames of discernment based on belief functions. IEEE Trans Fuzzy Syst, 29 (7): 1764- 1774.

[166] Liu ZG , Qiu GH , Mercier G , et al., 2021c. A transfer classification method for heterogeneous data based on evidence theory. IEEE Trans Syst Man Cybern Syst, 51 (8): 5129- 5141.

[167] Liu ZG , Duan JF , Huang LQ , et al., 2022a. Combination of classifiers with incomplete frames of discernment. Chin J Aeronaut, 35 (5): 145- 157.

[168] Liu ZG , Qiu GH , Wang SY , et al., 2022b. A new belief-based bidirectional transfer classification method. IEEE Trans Cybern, 52 (8): 8101- 8113.

[169] Luo H , Liu C , Wu C , et al., 2018. Urban change detection based on Dempster-Shafer theory for multitemporal very high-resolution imagery. Remote Sens, 10 (7): 980.

[170] Lv Y , Zhang BF , Zou GB , et al., 2022. Domain adaptation with data uncertainty measure based on evidence theory. Entropy, 24 (7): 966.

[171] Lv Y , Ma JP , Zhang YQ , et al., 2023. Integrating priors into domain adaptation based on evidence theory. Proc 32^nd ACM Int Conf on Information and Knowledge Management, p.1726- 1735.

[172] Ma CX , Liang Y , Zhang HX , et al., 2024. Distributed credible evidence fusion with privacy-preserving. Inform Fus, 112: 102571.

[173] Ma LY , Denœux T , 2021. Partial classification in the belief function framework. Knowl-Based Syst, 214: 106742.

[174] Masson MH , Denœux T , 2008. ECM:an evidential version of the fuzzy c-means algorithm. Patt Recogn, 41 (4): 1384- 1397.

[175] Meinert N , Gawlikowski J , Lavin A , 2023. The unreasonable effectiveness of deep evidential regression. Proc 37^th AAAI Conf on Artificial Intelligence, p.9134- 9142.

[176] Mullick SS , Datta S , Dhekane SG , et al., 2020. Appropriateness of performance indices for imbalanced data classification:an analysis. Patt Recogn, 102: 107197.

[177] Niu JW , Liu ZG , Lu Y , et al., 2022. Evidential combination of classifiers for imbalanced data. IEEE Trans Syst Man Cybern Syst, 52 (12): 7642- 7653.

[178] Niu ST , Liu YX , Wang J , et al., 2020. A decade survey of transfer learning (2010-2020). IEEE Trans Artif Intell, 1 (2): 151- 166.

[179] Pan QT , Li ZR , Yang G , et al., 2026. EviVLM:when evidential learning meets vision language model for medical image segmentation. IEEE Trans Med Imag, 45 (4): 1369- 1382.

[180] Petit-Renaud S , Denœux T , 2004. Nonparametric regression analysis of uncertain and imprecise data using belief functions. Int J Approx Reason, 35 (1): 1- 28.

[181] Qiao SH , Song BJ , Fan YS , et al., 2023. A fuzzy Dempster-Shafer evidence theory method with belief divergence for unmanned surface vehicle multi-sensor data fusion. J Mar Sci Eng, 11 (8): 1596.

[182] Ramel S , Pichon F , Delmotte F , 2020. A reliable version of choquistic regression based on evidence theory. Knowl-Based Syst, 205: 106252.

[183] Ribeiro MX , Traina AJM , Traina C , et al., 2008. An association rulebased method to support medical image diagnosis with efficiency. IEEE Trans Multim, 10 (2): 277- 285.

[184] Scheirer WJ , Jain LP , Boult TE , 2014. Probability models for open set recognition. IEEE Trans Patt Anal Mach Intell, 36 (11): 2317- 2324.

[185] Sebbak F , Chibani A , Amirat Y , et al., 2012. An evidential fusion approach for activity recognition under uncertainty in ambient intelligence environments. Proc ACM Conf on Ubiquitous Computing, p.834- 840.

[186] Sebbak F , Chibani A , Amirat Y , et al., 2013. An evidential fusion approach for activity recognition in ambient intelligence environments. Robot Auton Syst, 61 (11): 1235- 1245.

[187] Sehar U , Xiong J , Xia ZY , 2025. Automatic tooth labeling after segmentation using prototype-based meta-learning. Mach Intell Res, 22 (3): 539- 552.

[188] Sensoy M , Kaplan L , Kandemir M , 2018. Evidential deep learning to quantify classification uncertainty. Proc 32^nd Int Conf on Neural Information Processing Systems, p.3183- 3193.

[189] Shafer G , 1976. A Mathematical Theory of Evidence. Princeton University Press, Princeton, USA.

[190] Shafer G , 2016. A mathematical theory of evidence turns 40. Int J Approx Reason, 79: 7- 25.

[191] Shi YS , Gong CY , Su ZG , 2025. Time-series clustering with dynamic feature mining in the framework of belief function theory. IEEE Trans Syst Man Cybern Syst, 55 (9): 6136- 6149.

[192] Skau E , Armstrong C , Truong DP , et al., 2023. Open world Dempster-Shafer using complementary sets. Proc 13^th Int Symp on Imprecise Probability:Theories and Applications, p.438- 449.

[193] Smets P , 2005. Decision making in the TBM:the necessity of the pignistic transformation. Int J Approx Reason, 38 (2): 133- 147.

[194] Song XF , Zhang Y , Gong DW , et al., 2022. A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data. IEEE Trans Cybern, 52 (9): 9573- 9586.

[195] Strat TM , 1990. Decision analysis using belief functions. Int J Approx Reason, 4 (5-6): 391- 417.

[196] Su ZG , Denœux T , 2019. BPEC:belief-peaks evidential clustering. IEEE Trans Fuzzy Syst, 27 (1): 111- 123.

[197] Su ZG , Wang PH , 2014. Regression analysis of belief functions on interval-valued variables:comparative studies. Soft Comput, 18 (1): 51- 70.

[198] Su ZG , Wang PH , Yu XJ , 2010. Immune genetic algorithm-based adaptive evidential model for estimating unmeasured parameter:estimating levels of coal powder filling in ball mill. Expert Syst Appl, 37 (7): 5246- 5258.

[199] Su ZG , Wang YF , Wang PH , 2013. Parametric regression analysis of imprecise and uncertain data in the fuzzy belief function framework. Int J Approx Reason, 54 (8): 1217- 1242.

[200] Su ZG , Denœux T , Hao YS , et al., 2018. Evidential K-NN classification with enhanced performance via optimizing a class of parametric conjunctive t-rules. Knowl-Based Syst, 142: 7- 16.

[201] Su ZG , Hu QH , Denœux T , 2021. A distributed rough evidential K-NN classifier:integrating feature reduction and classification. IEEE Trans Fuzzy Syst, 29 (8): 2322- 2335.

[202] Sun QR , Zhou YY , Gong PL , et al., 2025. Attention detection using EEG signals and machine learning:a review. Mach Intell Res, 22 (2): 219- 238.

[203] Sun RL , Deng Y , 2019. A new method to identify incomplete frame of discernment in evidence theory. IEEE Access, 7: 15547- 15555.

[204] Tang SW , Zhou ZJ , Hu CH , et al., 2022. A new evidential reasoning rule-based safety assessment method with sensor reliability for complex systems. IEEE Trans Cybern, 52 (5): 4027- 4038.

[205] Tang SW , Cao Y , Jiang J , et al., 2023. Modeling, optimization, and robustness analysis of evidential reasoning rule under multidiscernment framework. IEEE Trans Aerosp Electron Syst, 59 (6): 8981- 8994.

[206] Thabtah F , Hammoud S , Kamalov F , et al., 2020. Data imbalance in classification:experimental evaluation. Inform Sci, 513: 429- 441.

[207] Tian HP , Zhang ZW , Martin A , et al., 2022. Reliability-based imbalanced data classification with Dempster-Shafer theory. Proc 7^th Int Conf on Belief Functions:Theory and Applications, p.77- 86.

[208] Tian SQ , Zhong YF , Ma AL , et al., 2022. Three-dimensional change detection in urban areas based on complementary evidence fusion. IEEE Trans Geosci Remote Sens, 60: 5608913.

[209] Tong Z , Xu P , Denœux T , 2019. ConvNet and Dempster-Shafer theory for object recognition. Proc 13^th Int Conf on Scalable Uncertainty Management, p.368- 381.

[210] Tong Z , Xu P , Denœux T , 2021b. Fusion of evidential CNN classifiers for image classification. Proc 6^th Int Conf on Belief Functions:Theory and Applications, p.168- 176.

[211] Wang GX , Cheng G , Zhou PC , et al., 2024. Cross-level attentive feature aggregation for change detection. IEEE Trans Circ Syst Video Technol, 34 (7): 6051- 6062.

[212] Wang J , Zhou ZJ , Hu CH , et al., 2022. A new evidential reasoning rule with continuous probability distribution of reliability. IEEE Trans Cybern, 52 (8): 8088- 8100.

[213] Wang JY , Yu QZ , 2020. A dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator. Appl Intell, 50 (11): 3837- 3851.

[214] Wang LC , Wang ZD , Han QL , et al., 2018. Event-based varianceconstrained H_∞ filtering for stochastic parameter systems over sensor networks with successive missing measurements. IEEE Trans Cybern, 48 (3): 1007- 1017.

[215] Wang M , Zhang XY , 2010. Change detection using high spatial resolution remotely sensed imagery by combining evidence theory and structural similarity. J Remote Sens, 14 (3): 558- 570 (in Chinese).

[216] Wang X , Rong Y , Wang SA , et al., 2025. Unleashing the power of CNN and Transformer for balanced RGB-event video recognition. Mach Intell Res, 22 (6): 1031- 1047.

[217] Williams D , Liao XJ , Xue Y , et al., 2007. On classification with incomplete data. IEEE Trans Patt Anal Mach Intell, 29 (3): 427- 436.

[218] Wu DQ , Li HH , Hou YX , et al., 2024. Spatial-channel attention Transformer with pseudo regions for remote sensing image-text retrieval. IEEE Trans Geosci Remote Sens, 62: 4704115.

[219] Xiao FY , 2019. Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy. Inform Fus, 46: 23- 32.

[220] Xiao FY , 2020. A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion. Inform Sci, 514: 462- 483.

[221] Xie MY , Han ZB , Zhang CQ , et al., 2023. Exploring and exploiting uncertainty for incomplete multi-view classification. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.19873- 19882.

[222] Xie YX , Qiu M , Zhang HB , et al., 2022. Gaussian distribution based oversampling for imbalanced data classification. IEEE Trans Knowl Data Eng, 34 (2): 667- 679.

[223] Xu NJ , Xue DZ , Qian SS , et al., 2025. Prompting large language models for automatic question tagging. Mach Intell Res, 22 (5): 917- 928.

[224] Xu PL , Cui YQ , Xiong W , 2025. MH-T2TA:a multiple-hypothesis algorithm for multi-sensor track-to-track association with an intelligent track score. Front Inform Technol Electron Eng, 26 (11): 2231- 2253.

[225] Xu SX , Chen YF , Ma C , et al., 2022. Deep evidential fusion network for medical image classification. Int J Approx Reason, 150: 188- 198.

[226] Xue GD , Chang Q , Wang J , et al., 2023. An adaptive neuro-fuzzy system with integrated feature selection and rule extraction for high-dimensional classification problems. IEEE Trans Fuzzy Syst, 31 (7): 2167- 2181.

[227] Yager RR , 1987. On the Dempster-Shafer framework and new combination rules. Inform Sci, 41 (2): 93- 137.

[228] Yan BW , Lang CB , Cheng G , et al., 2024. Understanding negative proposals in generic few-shot object detection. IEEE Trans Circ Syst Video Technol, 34 (7): 5818- 5829.

[229] Yang DP , Guo YF , Xue YB , et al., 2025. Distributed kernel mean embedding Gaussian belief propagation for underwater multi-sensor multi-target passive tracking. Front Inform Technol Electron Eng, 26 (10): 2016- 2029.

[230] Yang LH , Ren TY , Ye FF , et al., 2022. An ensemble extended belief rule base decision model for imbalanced classification problems. Knowl-Based Syst, 242: 108410.

[231] Yu H , Chen LY , Yao JT , 2021. A three-way density peak clustering method based on evidence theory. Knowl-Based Syst, 211: 106532.

[232] Yuan B , Yue XD , Lv Y , et al., 2020. Evidential deep neural networks for uncertain data classification. Proc 13^th Int Conf on Knowledge Science, Engineering and Management, p.427- 437.

[233] Zhang CL , Zhou ZJ , Luo JY , et al., 2023. Statistical feature-based evidential reasoning for equipment health state assessment. IEEE Trans Aerosp Electron Syst, 59 (6): 8219- 8234.

[234] Zhang JF , Zhang QH , He X , et al., 2021. Compound-fault diagnosis of rotating machinery:a fused imbalance learning method. IEEE Trans Contr Syst Technol, 29 (4): 1462- 1474.

[235] Zhang JW , Wang ZC , Li SF , et al., 2025. Convolutional neural network aided movable antenna array design for channel estimation. Intell Robot, 5 (4): 844- 58.

[236] Zhang JX , Cui Q , Ma XJ , 2024. Deep evidential remote sensing landslide image classification with a new divergence, multiscale saliency and an improved three-branched fusion. IEEE J Sel Top Appl Earth Obs Remote Sens, 17: 3799- 3820.

[237] Zhang WA , Chen MZQ , Liu AD , et al., 2017. Aperiodic optimal linear estimation for networked systems with communication uncertainties. IEEE Trans Cybern, 47 (8): 2256- 2265.

[238] Zhang YJ , Peng DF , Huang X , 2018. Object-based change detection for VHR images based on multiscale uncertainty analysis. IEEE Geosci Remote Sens Lett, 15 (1): 13- 17.

[239] Zhang Z , Zhou Y , Huang LQ , et al., 2025. Integrating local and global features via CNN and Mamba for semi-supervised change detection. IEEE Trans Instrum Meas, 74: 5034715.

[240] Zhang Z , Zhou Y , Huang LQ , et al., 2026. StyleDE-CD:dual-level style disentanglement for semi-supervised change detection. IEEE J Sel Top Appl Earth Obs Remote Sens, 19: 14203- 14218.

[241] Zhang ZW , Liu Z , Ma ZF , et al., 2021. Evidence integration credal classification algorithm versus missing data distributions. Inform Sci, 569: 39- 54.

[242] Zhang ZW , Ye ST , Zhang YR , et al., 2022a. Belief combination of classifiers for incomplete data. IEEE/CAA J Autom Sin, 9 (4): 652- 667.

[243] Zhang ZW , Tian HP , Yan LZ , et al., 2022b. Learning a credal classifier with optimized and adaptive multiestimation for missing data imputation. IEEE Trans Syst Man Cybern Syst, 52 (7): 4092- 4104.

[244] Zhao C , Du DW , Hoogs A , et al., 2023. Open set action recognition via multi-label evidential learning. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.22982- 22991.

[245] Zhao JX , Liu SW , Wan JH , et al., 2020. Change detection method of high resolution remote sensing image based on D-S evidence theory feature fusion. IEEE Access, 9: 4673- 4687.

[246] Zhou K , Martin A , Pan Q , et al., 2015. Median evidential c-means algorithm and its application to community detection. KnowlBased Syst, 74: 69- 88.

[247] Zhou K , Martin A , Pan Q , et al., 2016. ECMdd:evidential c-medoids clustering with multiple prototypes. Patt Recogn, 60: 239- 257.

[248] Zhou K , Martin A , Pan Q , et al., 2018. SELP:semi-supervised evidential label propagation algorithm for graph data clustering. Int J Approx Reason, 92: 139- 154.

[249] Zhou K , Guo M , Martin A , 2022. Evidential prototype-based clustering based on transfer learning. Int J Approx Reason, 151: 322- 343.

[250] Zhou K , Zhang ZX , Xu HM , et al., 2026. Reliability analysis based on evidential likelihood for uncertain mixed Weibull distribution. IEEE Trans Reliab, 75: 1020- 1034.

[251] Zhou N , Yao NM , Li QB , 2025. Neural network based on inter-layer perturbation strategy for text classification. Mach Intell Res, 22 (1): 176- 188.

[252] Zhou QL , Pedrycz W , Liang Y , et al., 2023. Information granule based uncertainty measure of fuzzy evidential distribution. IEEE Trans Fuzzy Syst, 31 (12): 4385- 4396.

[253] Zhou QL , Deng Y , Yager RR , 2024. CD-BFT:canonical decompositionbased belief functions transformation in possibility theory. IEEE Trans Cybern, 54 (1): 611- 623.

[254] Zhou QL , Pedrycz W , Deng Y , 2025a. Order-2 probabilistic information fusion on random permutation set. IEEE Trans Knowl Data Eng, 37 (2): 837- 850.

[255] Zhou QL , Zhu L , Deng Y , 2025b. Measure-based uncertainty with Dempster-Shafer structure. Sci China Inform Sci, 68 (10): 202204.

[256] Zhou XQ , Yue XD , Xu ZK , et al., 2022. PENet:prior evidence deep neural network for bladder cancer staging. Methods, 207: 20- 28.

[257] Zhou ZJ , Hu CH , Hu GY , et al., 2015. Hidden behavior prediction of complex systems under testing influence based on semiquantitative information and belief rule base. IEEE Trans Fuzzy Syst, 23 (6): 2371- 2386.

[258] Zhou ZJ , Hu GY , Zhang BC , et al., 2018. A model for hidden behavior prediction of complex systems based on belief rule base and power set. IEEE Trans Syst Man Cybern Syst, 48 (9): 1649- 1655.

[259] Zhu HW , Basir O , 2005. An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images. IEEE Trans Geosci Remote Sens, 43 (8): 1874- 1889.

[260] Zhu TY , Braytee A , Thiyagarajan K , et al., 2026. Autonomous detection of concrete cracks using self-supervised DinoV2. Mach Intell Res, 23 (1): 168- 184.

[261] Zhu YC , Zhou K , Cuzzolin F , 2026. TDCC:a trustworthy deep credal clustering method for uncertain data. IEEE Trans Cybern, 56 (5): 2876- 2887.

[262] Zhuang FZ , Qi ZY , Duan KY , et al., 2021. A comprehensive survey on transfer learning. Proc IEEE, 109 (1): 43- 76.

[263] Zouhal LM , Denœux T , 1998. An evidence-theoretic k-NN rule with parameter optimization. IEEE Trans Syst Man Cybern Part C (App Rev), 28 (2): 263- 271.

[264] Zuo KZ , Li XD , Dong YL , et al., 2023. Combination of differentgranularity beliefs for sensor-based human activity recognition. IEEE Sens J, 23 (11): 12309- 12319.

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