Multimodal Data Fusion Methods for Mixed Sets of Fuzzy and Precise Numbers
DOI:
https://doi.org/10.54097/7b1b8m05Keywords:
Data Fusion; Dempster-Shafer Evidence Theory; Bhattacharyya Jensen-Shannon Divergence; Information Theory.Abstract
This paper presents a novel data fusion framework to address the challenges of extracting valuable information from big data characterized by its "5V" attributes. The framework integrates Dempster-Shafer Evidence Theory (DSET) for handling fuzzy data and Information Theory for optimizing the fusion process, resulting in a comprehensive and robust approach. A new algorithm for data preprocessing and classification is proposed, utilizing K-means clustering to differentiate between fuzzy and precise data. Fuzzy data is processed using DSET, while the Bhattacharyya Jensen-Shannon Divergence (BJS divergence) is employed to quantify uncertainty and information loss more accurately than traditional methods. The framework includes an enhanced credibility evidence fusion algorithm that assigns higher credibility to well-supported evidence, ensuring reliable decision-making. Evaluated using a medical dataset, the framework demonstrates significant improvements in performance metrics such as accuracy and -score. The study concludes that the proposed method effectively bridges the gap between fuzzy labels and precise classification, offering a universal solution for data fusion that outperforms existing approaches and sets a new benchmark for future research in data fusion technology.
Downloads
References
[1] Wei, Y. , Cheng, Z. , Zhu, B. , Zhai, X. , & Zhang, H. . (2019). Infrared and radar fusion detection method based on heterogeneous data preprocessing. Optical and Quantum Electronics, 51(10). https://doi.org /10.1007/s11082-019-2053-z
[2] Zhang, P. , Liu, W. , Lei, Y. , & Lu, H. . (2019). Hyperfusion-net: hyper-densely reflective feature fusion for salient object detection. Pattern Recognition, 93(4). https://doi.org/ 10.1016/j.patcog.2019.05.012
[3] A, F. J. , A, X. W. , B, J. G. , A, Q. L. , C, J. Z. A. , & A, H. L. , et al. (2020). Bi-connect net for salient object detection. Neurocomputing, 384, 142-155. https://doi.org/ 10.1016/j.neucom.2019.12.020
[4] Khaleghi, B. , Khamis, A. , Karray, F. O. , & Razavi, S. N. . (2013). Multisensor data fusion: a review of the state-of-the-art. Information Fusion, 14(1), 28-44. https://doi.org/ 10.1016/j.inffus.2011.08.001
[5] Zheng, H. , Cai, A. , Zhou, Q. , Xu, P. , Zhao, L. , & Li, C. , et al. (2017). Optimal preprocessing of serum and urine metabolomic data fusion for staging prostate cancer through design of experiment. Analytica Chimica Acta, 68-75. https://doi.org/10.1016/j.aca.2017.09.019
[6] Dolly, D. R. J. , Peter, J. D. , Bala, G. J. , & Jagannath, D. J. (2020). Image fusion for stabilized medical video sequence using multimodal parametric registration. Pattern Recognition Letters, 135. https://doi.org/10.1016/j.patrec.2020.04.001
[7] Zhang, H. , Ananda, R. , Fu, X. , Sun, Z. , Wang, X. , & Chen, K. , et al. (2023). Multi-channel sensor network construction, data fusion and challenges for smart home.
[8] Maimaitijiang, M. , Sagan, V. , Sidike, P. , Hartling, S. , & Fritschi, F. B. . (2019). Soybean yield prediction from uav using multimodal data fusion and deep learning. Remote Sensing of Environment, 237. https://doi.org/10.1016/j.rse.2019.111599
[9] Liu, X. , Liu, Q. , & Wang, Y. . (2019). Remote sensing image fusion based on two-stream fusion network. Information Fusion, 55. https://doi.org/10.1016/j.inffus.2019.07.010
[10] Rajah, P. , Odindi, J. , & Mutanga, O. . (2018). Feature level image fusion of optical imagery and synthetic aperture radar (sar) for invasive alien plant species detection and mapping. Remote Sensing Applications Society and Environment, 10, 198–208.
[11] Huang, M. , Liu, Z. , & Tao, Y. . (2019). Mechanical fault diagnosis and prediction in iot based on multi-source sensing data fusion. Simulation Modelling Practice and Theory, 102：101981. https://doi.org/10.1016/j.simpat.2019.101981
[12] Gong, X. , Ding, L. , Du, W. , & Wang, H. . (2017). Gear fault diagnosis using dual channel data fusion and eemd method. Procedia Engineering, 174(Complete), 918-926. https://doi.org/10.1016/j.proeng.2017.01.242
[13] Safizadeh, M. S. , & Latifi, S. K. . (2014). Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Information Fusion, 18, 1-8. https://doi.org/10.1016/j.inffus.2013.10.002
[14] Yan, J. , Hu, Y. , & Guo, C. . (2019). Rotor unbalance fault diagnosis using dbn based on multi-source heterogeneous information fusion. Procedia Manufacturing, 35, 1184-1189. https://doi.org/10.1016/j.promfg.2019.06.075
[15] Vita, F. D. , Bruneo, D. , & Das, S. K. . (2020). On the use of a full stack hardware/software infrastructure for sensor data fusion and fault prediction in industry 4.0. Pattern Recognition Letters,138：30-37. https://doi.org/10.1016/j.patrec.2020.06.028
[16] Rato, T. J. ,& Reis, M. S. . (2019). Optimal fusion of industrial data streams with different granularities. Computers & Chemical Engineering, 130(Nov.2), 106564.1-106564.16. https://doi.org/10.1016/j.compchemeng.2019.106564
[17] Alofi, A. , Alghamdi, A. , Alahmadi, R. , Aljuaid, N. , & Hemalatha, M. . (2017). A review of data fusion techniques. International Journal of Computer Applications, 167(7), 37-41.
[18] Mcgurk, H. , & Macdonald, J. . (1976). Hearing lips and seeing voices. Nature, 264(5588), 746-748. https://doi.org/10.1038/264746a0
[19] Dai, Y. , Yan, Z. , Cheng, J. , Duan, X. , & Wang, G. . (2023). Analysis of multimodal data fusion from an information theory perspective. Information Sciences, 623, 164-183. https://doi.org/10.1016/j.ins.2022.12.014
[20] Song, M. , Sun, C. , Cai, D. , Hong, S. , & Li, H. . (2022). Classifying vaguely labeled data based on evidential fusion. Information Sciences: An International Journal(583-), 583, 159–173. https://doi.org/ 10.1016/j.ins.2021.11.005
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Academic Journal of Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
