-
CiteScore
1.83
Impact Factor
Chinese Journal of Information Fusion, 2024, Volume 1, Issue 2: 134-159

Free Access | Review Article | Feature Paper | 30 September 2024 | Cited: 1
1 School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China
2 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
3 Systems Research Institute, Polish Academy of Sciences, 00-901 Warsaw, Poland
4 National Information Processing Institute, 00-608 Warsaw, Poland
5 Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
* Corresponding author: Fuyuan Xiao, email: [email protected]
Received: 30 July 2024, Accepted: 28 September 2024, Published: 30 September 2024  

Abstract
Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory and its extensions, collectively referred to as classical evidence theory, from three aspects of uncertainty modeling, fusion, and decision making. Next, we study and explore complex evidence theory for data fusion in both closed world and open world contexts that benefits from the frame of complex plane modelling. We then present classical and complex evidence theory framework-based multisource data fusion algorithms, which are applied to pattern classification to compare and demonstrate their applicabilities. The research results indicate that the complex evidence theory framework can enhance the capabilities of uncertainty modeling and reasoning by generating constructive interference through the fusion of appropriate complex basic belief assignment functions modeled by complex numbers. Through analysis and comparison, we finally propose several challenges and identify open future research directions in evidence theorybased data fusion.

Graphical Abstract
Complex Evidence Theory for Multisource Data Fusion

Keywords
multisource data fusion
Dempster--Shafer evidence theory
complex evidence theory
quantum evidence theory
uncertainty modeling
conflict management
belief function
decision making
pattern classification

References

[1] He, Y., Yao, L., and Jiang, Z. (2019). Summary and future development of marine target surveillance based on spatial information network. Journal on Communications, 40(4): 9.

[2] Zhou, G., Bu, S., & Kirubarajan, T. (2024). Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems. Chinese Journal of Information Fusion, 1(1), 16-32.

[3] Lai, J. W., Chang, J., Ang, L. K., & Cheong, K. H. (2020). Multi-level information fusion to alleviate network congestion. Information Fusion, 63, 248-255.

[4] Guo, X., Yang, F., & Ji, L. (2024). A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory. Chinese Journal of Information Fusion, 1(1), 33-49.

[5] Yang, J. B., Xu, D. L., Xu, X., & Fu, C. (2023). Likelihood analysis of imperfect data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(8), 5046-5057.

[6] Cao, B., Li, C., Song, Y., Qin, Y., & Chen, C. (2022). Network intrusion detection model based on CNN and GRU. Applied Sciences, 12(9), 4184.

[7] Miao, W., Xu, Z., Geng, J., and Jiang, W. (2023). Ecae: Edge-aware class activation enhancement for semisupervised remote sensing image semantic segmentation. IEEE Transactions on Geoscience and Remote Sensing, 61: 1–14.

[8] Fan, H., Lu, D., Jiang, Y., and Lilienthal, A. J. (2024). Extraction of motion information from occupancy grid map using keystone transform. Chinese Journal of Information Fusion, 1(1): 63–78.

[9] Charte, D., Charte, F., García, S., del Jesus, M. J., and Herrera, F. (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Information Fusion, 44: 78–96.

[10] Liu, J., Chen, Z., Zhou, J., Xue, A., Peng, D., Gu, Y., and Chen, H. (2024). Research on a ship trajectory classification method based on deep learning. Chinese Journal of Information Fusion, 1(1): 3–15.

[11] Deng, X., Xue, S., and Jiang, W. (2023). A novel quantum model of mass function for uncertain information fusion. Information Fusion, 89: 619–631.

[12] Jin, X., Liu, S., Kong, J., Bai, Y., Su, T., and Ma, H. (2024). GPS tracking based on stacked-serial LSTM network. Chinese Journal of Information Fusion, 1(1): 50–62.

[13] Kang, B. and Zhao, C. (2024). Deceptive evidence detection in information fusion of belief functions based on reinforcement learning. Information Fusion, 103: 102102.

[14] Li, T., Kong, L., Yang, X., Wang, B., and Xu, J. (2024). Bridging modalities: A survey of cross-modal image-text retrieval. Chinese Journal of Information Fusion, 1(1): 79–92.

[15] Wang, X., Zhu, D., Li, G., Zhang, X.-P., and He, Y. (2022a). Proposal-copula-based fusion of spaceborne and airborne SAR images for ship target detection**. Information Fusion, 77: 247–260.

[16] Chenghai, L. I., Ke, W. A. N. G., Yafei, S. O. N. G., Peng, W. A. N. G., & Lemin, L. I. (2024). Air target intent recognition method combining graphing time series and diffusion models. Chinese Journal of Aeronautics.

[17] Zhang, Y., Wang, X., Jiang, Z., Li, G., and He, Y. (2022b). An efficient center-based method with multilevel auxiliary supervision for multiscale SAR ship detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 7065–7075.

[18] Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., and Tan, U.-X. (2019). A survey of data fusion in smart city applications. Information Fusion, 52: 357–374.

[19] Ding, W., Jing, X., Yan, Z., and Yang, L. T. (2019). A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion. Information Fusion, 51: 129–144.

[20] Deng, X., Jiang, Y., Yang, L. T., Lin, M., Yi, L., and Wang, M. (2019). Data fusion based coverage optimization in heterogeneous sensor networks: A survey. Information Fusion, 52: 90–105.

[21] Ghamisi, P., Rasti, B., Yokoya, N., Wang, Q., Hofle, B., Bruzzone, L., Bovolo, F., Chi, M., Anders, K., Gloaguen, R., et al. (2019). Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art. IEEE Geoscience and Remote Sensing Magazine, 7(1): 6–39.

[22] Meng, T., Jing, X., Yan, Z., and Pedrycz, W. (2020). A survey on machine learning for data fusion. Information Fusion, 57: 115–129.

[23] El Fissaoui, M., Beni-hssane, A., Ouhmad, S., and El Makkaoui, K. (2021). A survey on mobile agent itinerary planning for information fusion in wireless sensor networks. Archives of computational methods in engineering, 28(3): 1323–1334.

[24] Zhang, Y., Jiang, C., Yue, B., Wan, J., and Guizani, M. (2022a). Information fusion for edge intelligence: A survey. Information Fusion, 81: 171–186.

[25] Xinde, L. I., DUNKIN, F., & DEZERT, J. (2023). Multi-source information fusion: Progress and future. Chinese Journal of Aeronautics.

[26] Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38(2): 325–339.

[27] Shafer, G. et al. (1976). A mathematical theory of evidence, volume 1. Princeton University Press Princeton.

[28] Zhang, Z., Ye, S., Zhang, Y., Ding, W., and Wang, H. (2022c). Belief combination of classifiers for incomplete data. IEEE/CAA Journal of Automatica Sinica, 9(4): 652–667.

[29] Fujita, H. and Ko, Y.-C. (2020). A heuristic representation learning based on evidential memberships: Case study of UCI-SPECTF. International Journal of Approximate Reasoning, 120.

[30] Yager, R. R. and Liu, L. (2008). Classic works of the Dempster–Shafer theory of belief functions, volume 219. Springer.

[31] Xiong, L., Su, X., and Qian, H. (2021). Conflicting evidence combination from the perspective of networks. Information Sciences, 580: 408–418.

[32] Liu, P., Li, Y., Zhang, X., and Pedrycz, W. (2022a). A multiattribute group decision-making method with probabilistic linguistic information based on an adaptive consensus reaching model and evidential reasoning. IEEE Transactions on Cybernetics, 53(3): 1905–1919.

[33] Xu, X., Zheng, J., Yang, J.-b., Xu, D.-l., and Chen, Y.-w. (2017). Data classification using evidence reasoning rule. Knowledge-Based Systems, 116: 144–151.

[34] Tang, S.-W., Zhou, Z.-J., Hu, C.-H., Yang, J.-B., and Cao, Y. (2019). Perturbation analysis of evidential reasoning rule. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(8): 4895–4910.

[35] Zhang, B., Zhang, Y., Hu, G., Zhou, Z., Wu, L., and Lv, S. (2020). A method of automatically generating initial parameters for large-scale belief rule base. Knowledge-Based Systems, 199: 105904.

[36] Fu, C., Hou, B., Xue, M., Chang, L., and Liu, W. (2023). Extended belief rule-based system with accurate rule weights and efficient rule activation for diagnosis of thyroid nodules. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(1): 251–263.

[37] Zhou, Z., Hu, G., Hu, C., Wen, C., and Chang, L. (2019). A survey of belief rule-base expert system. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(8): 4944–4958.

[38] Chang, L., Zhang, L., Fu, C., and Chen, Y.-W. (2021). Transparent digital twin for output control using belief rule base. IEEE Transactions on Cybernetics, 52(10): 10364–10378.

[39] Cao, Y., Zhou, Z., Hu, C., He, W., and Tang, S. (2020). On the interpretability of belief rule-based expert systems. IEEE Transactions on Fuzzy Systems, 29(11): 3489–3503.

[40] Xu, X., Guo, H., Zhang, Z., Shi, P., Huang, W., Li, X., and Brunauer, G. (2024a). Fault diagnosis method via one vs rest evidence classifier considering imprecise feature samples. Applied Soft Computing, page 111761.

[41] Xu, X., Guo, H., Zhang, Z., Yu, S., Chang, L., Steyskal, F., and Brunauer, G. (2024b). A cloud model-based interval-valued evidence fusion method and its application in fault diagnosis. Information Sciences, 658: 119995.

[42] Chen, X. and Deng, Y. (2024). Evidential software risk assessment model on ordered frame of discernment. Expert Systems with Applications, 250: 123786.

[43] Zhou, M., Zheng, Y.-Q., Chen, Y.-W., Cheng, B.-Y., Herrera-Viedma, E., and Wu, J. (2023). A large-scale group consensus reaching approach considering self-confidence with two-tuple linguistic trust/distrust relationship and its application in life cycle sustainability assessment. Information Fusion, 94: 181–199.

[44] Fei, L., Liu, X., and Zhang, C. (2024). An evidential linguistic ELECTRE method for selection of emergency shelter sites. Artificial Intelligence Review, 57(4): 81.

[45] Zadeh, L. A. (1986). A simple view of the Dempster–Shafer theory of evidence and its implication for the rule of combination. AI Magazine, 7(2): 85.

[46] Smets, P. (1990). The combination of evidence in the transferable belief model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5): 447–458.

[47] Dezert, J. and Smarandache, F. (2006). DSmT: A new paradigm shift for information fusion. In Proceedings of Cogis’06 Conference.

[48] Deng, Y. (2015). Generalized evidence theory. Applied Intelligence, 43(3): 530–543.

[49] Smarandache, F. and Dezert, J. (2015). Advances and Applications of DSmT for Information Fusion, Vol. IV: Collected Works. Infinite Study.

[50] Deng, Y. (2022). Random permutation set. International Journal of Computers Communications & Control, 17(1): 4542.

[51] Deng, J., Deng, Y., and Yang, J.-B. (2024). Random permutation set reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence, page DOI: 10.1109/TPAMI.2024.3438349.

[52] Xiao, F. (2023). Generalized quantum evidence theory. Applied Intelligence, 53(11), 14329-14344.

[53] Deng, X. and Jiang, W. (2023). A framework for the fusion of non-exclusive and incomplete information on the basis of D number theory. Applied Intelligence, 53: 11861–11884.

[54] Kouatli, I. (2022). The use of fuzzy logic as augmentation to quantitative analysis to unleash knowledge of participants’ uncertainty when filling a survey: case of cloud computing. IEEE Transactions on Knowledge and Data Engineering, 34(3): 1489–1500.

[55] Akcora, C. G., Gel, Y. R., Kantarcioglu, M., Lyubchich, V., and Thuraisingham, B. (2021). Graphboot: Quantifying uncertainty in node feature learning on large networks. IEEE Transactions on Knowledge and Data Engineering, 33(1): 116–127.

[56] Fei, L. and Wang, Y. (2022). An optimization model for rescuer assignments under an uncertain environment by using Dempster–Shafer theory. Knowledge-Based Systems, 255: 109680.

[57] An, L., Li, M., Boudaren, M. E. Y., and Pieczynski, W. (2018). Unsupervised segmentation of hidden Markov fields corrupted by correlated non-Gaussian noise. International Journal of Approximate Reasoning, 102: 41–59.

[58] Zhang, Z.-W., Liu, Z.-G., Martin, A., and Zhou, K. (2022d). BSC: Belief Shift Clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(3): 1748–1760.

[59] Denoeux, T. (2021). NN-EVCLUS: Neural network-based evidential clustering. Information Sciences, 572: 297–330.

[60] Zhou, K., Martin, A., Pan, Q., and Liu, Z. (2018). SELP: Semi-supervised evidential label propagation algorithm for graph data clustering. International Journal of Approximate Reasoning, 92: 139–154.

[61] He, H., Han, D., and Dezert, J. (2020). Disagreement based semi-supervised learning approaches with belief functions. Knowledge-Based Systems, 193: 105426.

[62] Antoine, V., Guerrero, J. A., and Xie, J. (2021). Fast semi-supervised evidential clustering. International Journal of Approximate Reasoning, 133: 116–132.

[63] Xu, P., Davoine, F., Zha, H., & Denoeux, T. (2016). Evidential calibration of binary SVM classifiers. International Journal of Approximate Reasoning, 72, 55-70.

[64] Denœux, T. (2019). Logistic regression, neural networks and dempster–shafer theory: A new perspective. Knowledge-Based Systems, 176: 54–67.

[65] Tong, Z., Xu, P., and Denoeux, T. (2021). An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing, 450: 275–293.

[66] Yager, R. R. (1987). On the Dempster–Shafer framework and new combination rules. Information Sciences, 41(2): 93–137.

[67] Dubois, D. and Prade, H. (1988). Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence, 4(3): 244–264.

[68] Inagaki, T. (1991). Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory. IEEE Transactions on Reliability, 40(2): 182–188.

[69] Lefevre, E., Colot, O., and Vannoorenberghe, P. (2002). Belief function combination and conflict management. Information Fusion, 3(2): 149–162.

[70] Zhang, L. (1994). Representation, independence, and combination of evidence in the Dempster-Shafer theory. In Advances in the Dempster-Shafer theory of evidence, pages 51–69.

[71] Mahler, R. P. (1996). Combining ambiguous evidence with respect to ambiguous a priori knowledge. i. boolean logic. IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans, 26(1): 27–41.

[72] Jiang, W. and Zhan, J. (2017). A modified combination rule in generalized evidence theory. Applied Intelligence, 46(3): 630–640.

[73] Xiao, F. (2019). Generalization of Dempster–Shafer theory: A complex mass function. Applied Intelligence, 50(10): 3266–3275.

[74] Xiao, F. (2020a). Generalized belief function in complex evidence theory. Journal of Intelligent & Fuzzy Systems, 38(4): 3665–3673.

[75] Chen, X. and Deng, Y. (2023). A novel combination rule for conflict management in data fusion. Soft Computing, 27(22): 16483–16492.

[76] Jousselme, A.-L., Grenier, D., and Bossé, É. (2001). A new distance between two bodies of evidence. Information Fusion, 2(2): 91–101.

[77] Jousselme, A.-L. and Maupin, P. (2012). Distances in evidence theory: Comprehensive survey and generalizations. International Journal of Approximate Reasoning, 53(2): 118–145.

[78] Han, D., Dezert, J., and Yang, Y. (2016). Belief interval-based distance measures in the theory of belief functions. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(6): 833–850.

[79] Smets, P. and Kennes, R. (1994). The transferable belief model. Artificial Intelligence, 66(2): 191–234.

[80] Liu, W. (2006). Analyzing the degree of conflict among belief functions. Artificial Intelligence, 170(11): 909–924.

[81] Jiang, W. (2018). A correlation coefficient for belief functions. International Journal of Approximate Reasoning, 103: 94–106.

[82] Xiao, F., Wen, J., and Pedrycz, W. (2023b). Generalized divergence-based decision making method with an application to pattern classification. IEEE Transactions on Knowledge and Data Engineering, 35(7): 6941–6956.

[83] Xiao, F. (2023a). GEJS: A generalized evidential divergence measure for multisource information fusion. IEEE Transactions on Systems, Man, and Cybernetics - Systems, 53(4): 2246–2258.

[84] Huang, Y., Xiao, F., Cao, Z., and Lin, C.-T. (2023c). Higher order fractal belief Rényi divergence with its applications in pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12): 14709–14726.

[85] Zhang, L. and Xiao, F. (2024). Belief Rényi divergence of divergence and its application in time series classification. IEEE Transactions on Knowledge and Data Engineering, page DOI: 10.1109/TKDE.2024.3369719.

[86] Huang, Y., Xiao, F., Cao, Z., and Lin, C.-T. (2023b). Fractal belief Rényi divergence with its applications in pattern classification. IEEE Transactions on Knowledge and Data Engineering, page DOI: 10.1109/TKDE.2023.3342907.

[87] Deng, Y. (2020b). Uncertainty measure in evidence theory. SCIENCE CHINA Information Sciences, 63(11):210201.

[88] Abellán, J. (2017). Analyzing properties of Deng entropy in the theory of evidence. Chaos, Solitons & Fractals, 95: 195–199.

[89] Deng, Y. (2020a). Information volume of mass function. International Journal of Computers Communications & Control, 15(6): 3983.

[90] Liao, H., Ren, Z., and Fang, R. (2020). A Deng-entropy-based evidential reasoning approach for multi-expert multi-criterion decision-making with uncertainty. International Journal of Computational Intelligence Systems, 13(1): 1281–1294.

[91] Zhao, T., Li, Z., and Deng, Y. (2024). Linearity in Deng entropy. Chaos, Solitons & Fractals, 178: 114388.

[92] Cui, Y. and Deng, X. (2023). Plausibility entropy: a new total uncertainty measure in evidence theory based on plausibility function. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(6): 3833–3844.

[93] Qiang, C., Deng, Y., and Cheong, K. H. (2022). Information fractal dimension of mass function. Fractals, 30: 2250110.

[94] Zhu, L., Zhou, Q., Deng, Y., and Cheong, K. H. (2024). Fractal-based basic probability assignment: A transient mass function. Information Sciences, 652: 119767.

[95] Li, D., Deng, Y., and Cheong, K. H. (2021). Multisource basic probability assignment fusion based on information quality. International Journal of Intelligent Systems, 36(4): 1851–1875.

[96] Daniel, M. (2010). Conflicts within and between belief functions. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 696–705. Springer.

[97] Lefèvre, E. and Elouedi, Z. (2013). How to preserve the conflict as an alarm in the combination of belief functions? Decision Support Systems, 56: 326–333.

[98] Abellán, J. and Bossé, É. (2016). Drawbacks of uncertainty measures based on the pignistic transformation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(3): 382–388.

[99] Sudano, J. and Martin, L. (2006). Yet another paradigm illustrating evidence fusion (yapief). In 2006 9th International Conference on Information Fusion, pages 1–7. IEEE.

[100] Cuzzolin, F. (2007). Two new bayesian approximations of belief functions based on convex geometry. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(4): 993–1008.

[101] Han, D., Dezert, J., and Duan, Z. (2015). Evaluation of probability transformations of belief functions for decision making. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(1): 93–108.

[102] Xiao, F., Cao, Z., and Lin, C.-T. (2023a). A complex weighted discounting multisource information fusion with its application in pattern classification. IEEE Transactions on Knowledge and Data Engineering, 53(4): 2246–2258.

[103] Zhang, S., Yin, M., Xiao, F., Cao, Z., and Pelusi, D. (2024). A complex Gaussian fuzzy numbers-based multisource information fusion for pattern classification. IEEE Transactions on Fuzzy Systems, page DOI: 10.1109/TFUZZ.2024.3352615.

[104] Zhang, S. and Xiao, F. (2022). A TFN-based uncertainty modeling method in complex evidence theory for decision making. Information Sciences, page DOI: 10.1016/j.ins.2022.11.014.

[105] Yang, X. and Xiao, F. (2024). A novel uncertainty modeling method in complex evidence theory for decision making. Engineering Applications of Artificial Intelligence, 133: 108164.

[106] Wu, K. and Xiao, F. (2024). A novel quantum belief entropy for uncertainty measure in complex evidence theory. Information Sciences, 652: 119744.

[107] Huang, J., Fan, Y., and Xiao, F. (2023a). On some bridges to complex evidence theory. Engineering Applications of Artificial Intelligence, 117: 105605.

[108] Gao, L., Xiao, F., and Pelusi, D. (2023). A complex belief χ2 divergence in complex evidence theory and its application for pattern classification. Engineering Applications of Artificial Intelligence, 126: 106752.

[109] Liu, Z.-g., Fu, Y.-m., Pan, Q., and Zhang, Z.-w. (2022b). Orientational distribution learning with hierarchical spatial attention for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(7): 8757 – 8772.

[110] Liu, Z.-G., Qiu, G.-H., Wang, S.-Y., Li, T.-C., and Pan, Q. (2021). A new belief-based bidirectional transfer classification method. IEEE Transactions on Cybernetics, 52(8): 8101–8113.

[111] Xiao, F. and Pedrycz, W. (2023). Negation of the quantum mass function for multisource quantum information fusion with its application to pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2): 2054–2070.

[112] Xiao, F. (2020b). A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion. Information Sciences, 514: 462–483.

[113] Hu, B.-G. (2013). What are the differences between Bayesian classifiers and mutual-information classifiers? IEEE Transactions on Neural Networks and Learning Systems, 25(2): 249–264.

[114] Veenman, C. J. and Reinders, M. J. (2005). The nearest subclass classifier: A compromise between the nearest mean and nearest neighbor classifier. IEEE Trans. Pattern Anal. Mach. Intell., 27(9): 1417–1429.

[115] Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1): 21–27.

[116] Freund, Y., & Mason, L. (1999, June). The alternating decision tree learning algorithm. In icml (Vol. 99, pp. 124-133).

[117] Chang, C.-C. and Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3): 1–27.

[118] Castro, C. L. and Braga, A. P. (2013). Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data. IEEE Transactions on Neural Networks and Learning Systems, 24(6): 888–899.

[119] Chen, S., Cowan, C. F., and Grant, P. M. (1991). Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 2(2): 302–309.

[120] Denœux, T. (1995). A k-nearest neighbor classification rule based on Dempster–Shafer theory. IEEE Transactions on Systems, Man, and Cybernetics, 25(5): 804–813.

[121] Xu, P., Deng, Y., Su, X., and Mahadevan, S. (2013). A new method to determine basic probability assignment from training data. Knowledge-Based Systems, 46: 69–80.

[122] Wang, Y. M., Pan, X. H., He, S. F., Dutta, B., García-Zamora, D., & Martínez, L. (2022). A new decision-making framework for site selection of electric vehicle charging station with heterogeneous information and multigranular linguistic terms. IEEE Transactions on Fuzzy Systems, 31(2), 485-499.

[123] Xiao, F. (2023b). Quantum X-entropy in generalized quantum evidence theory. Information Sciences, 643: 119177.

[124] He, H. and Xiao, F. (2024). A novel quantum Dempster’s rule of combination for pattern classification. Information Sciences, 671: 120617.


Cite This Article
APA Style
Xiao, F., Wen, J., Pedrycz, W., & Aritsugi, M. (2024). Complex Evidence Theory for Multisource Data Fusion. Chinese Journal of Information Fusion, 1(2), 134–159. https://doi.org/10.62762/CJIF.2024.999646

Article Metrics
Citations:

Crossref

0

Scopus

1

Web of Science

1
Article Access Statistics:
Views: 842
PDF Downloads: 113

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Chinese Journal of Information Fusion

Chinese Journal of Information Fusion

ISSN: 2998-3371 (Online) | ISSN: 2998-3363 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.