Basic Belief Assignment Determination Based on Radial Basis Function Network

Wei Li; Deqiang Han; Jean Dezert; Yi Yang

doi:10.62762/CJIF.2024.841250

CiteScore

2.31

Impact Factor

Volume 1, Issue 3, Chinese Journal of Information Fusion

Volume 1, Issue 3, 2024

Submit Manuscript Edit a Special Issue

Academic Editor

Jun Liu

Hangzhou Dianzi University, China

Article QR Code

Scan the QR code for reading

Popular articles

Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Visual Feature Extraction and Tracking Method Based on Corner Flow Detection Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Inaugural Editorial of the Chinese Journal of Information Fusion Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform

Chinese Journal of Information Fusion, 2024, Volume 1, Issue 3: 175-182

Free to Read | Research Article | 07 December 2024

Basic Belief Assignment Determination Based on Radial Basis Function Network

Wei Li 1

Deqiang Han 1 *

Jean Dezert 2

Yi Yang 3

1 School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China

2 The French Aerospace Lab, Chemin de la Hunière, F-91761 Palaiseau, France

3 School of Aerospace, Xi’an Jiaotong University, Xi’an 710049, China

* Corresponding Author: Deqiang Han, [email protected]

DOI: https://doi.org/10.62762/CJIF.2024.841250

Received: 25 August 2024, Accepted: 22 October 2024, Published: 07 December 2024

PDF (848.04 KB)

Article Metrics Cite This Article

Abstract

In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the \enquote{compound-class samples} with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the singleton and compound ones).Experimental results of evidence decision-based pattern classification on multiple UCI and image datasets show that our proposed method is rational and effective.

Graphical Abstract

Basic Belief Assignment Determination Based on Radial Basis Function Network

Keywords

Dempster-Shafer evidence theory

basic belief assignment

uncertainty modeling

radial basis function network

pattern classification

Funding

This work was supported by National Natural Science Foundation of China under Grant 62473304 and Grant U22A2045.

Cite This Article

APA Style

Li, W., Han, D., Dezert, J., & Yang, Y. (2024). Basic Belief Assignment Determination Based on Radial Basis Function Network. Chinese Journal of Information Fusion, 1(3), 175–182. https://doi.org/10.62762/CJIF.2024.841250

References

Dempster, A.P. (1967) Upper and Lower Probabilities Induced by a Multivalued Mapping. The Annals of Mathematical Statistics, 38, 325-339.
[Google Scholar]
Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.
[Google Scholar]
Richter, S., Bieder, F., Wirges, S., Kinzig, C., & Stiller, C. (2022, July). Sensor data fusion in top-view grid maps using evidential reasoning with advanced conflict resolution. In 2022 25th International Conference on Information Fusion (FUSION) (pp. 1-7). IEEE.
[Google Scholar]
Sharma, M., & Maity, T. (2022, November). Fault-tolerant multi-sensor data fusion system for underground mine gas hazard prediction using Dempster Shafer Evidence Theory. In 2022 IEEE 6th Conference on Information and Communication Technology (CICT) (pp. 1-5). IEEE.
[Google Scholar]
Jin, J., Qu, T., Xu, R., Wang, X., & Cichocki, A. (2022). Motor imagery EEG classification based on Riemannian sparse optimization and dempster-shafer fusion of multi-time-frequency patterns. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 58-67.
[Google Scholar]
Zhao, J., Shi, Y., Liu, W., Zhou, T., Li, Z., & Li, X. (2023). A hybrid method fusing frequency recognition with attention detection to enhance an asynchronous brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 2391-2398.
[Google Scholar]
Han, D., Dezert, J., Tacnet, J. M., & Han, C. (2012, July). A fuzzy-cautious OWA approach with evidential reasoning. In 2012 15th International Conference on Information Fusion (pp. 278-285). IEEE.
[Google Scholar]
Xiao, F., Wen, J., & Pedrycz, W. (2022). Generalized divergence-based decision making method with an application to pattern classification. IEEE transactions on knowledge and data engineering, 35(7), 6941-6956.
[Google Scholar]
Han, D., Yang, Y., & Han, C. (2014). Advances in DS evidence theory and related discussions. Control and Decision, 29(1), 1-11.
[Google Scholar]
Selzer, F., & Gutfinger, D. (1989, January). LADAR and FLIR based sensor fusion for automatic target classification. In Sensor fusion: Spatial reasoning and scene interpretation (Vol. 1003, pp. 236-246). SPIE.
[Google Scholar]
Bi, Y., Bell, D., & Guan, J. (2004). Combining evidence from classifiers in text categorization. In Knowledge-Based Intelligent Information and Engineering Systems: 8th International Conference, KES 2004, Wellington, New Zealand, September 20-25, 2004, Proceedings, Part III 8 (pp. 521-528). Springer Berlin Heidelberg.
[Google Scholar]
Zhang, Z., Han, D., & Yang, Y. (2015, October). Image segmentation based on evidential Markov random field model. In 2015 International Conference on Control, Automation and Information Sciences (ICCAIS) (pp. 239-244). IEEE.
[Google Scholar]
Dezert, J., Liu, Z. G., & Mercier, G. (2011, July). Edge detection in color images based on DSmT. In 14th International Conference on Information Fusion (pp. 1-8). IEEE.
[Google Scholar]
Jiang, W., Duanmu, D., Fan, X., & Deng, Y. (2012, May). A new method to determine basic probability assignment under fuzzy environment. In 2012 International Conference on Systems and Informatics (ICSAI2012) (pp. 758-762). IEEE.
[Google Scholar]
Han, D., Han, C., & Deng, Y. (2013). Novel approaches for the transformation of fuzzy membership function into basic probability assignment based on uncertainty optimization. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(02), 289-322.
[Google Scholar]
Kang, B. Y., Li, Y. A., Deng, Y., Zhang, Y. J., & assignment based on interval numbers and its application. Dianzi Xuebao(Acta Electronica Sinica), 40(6), 1092-1096.
[Google Scholar]
Zhang, S., Han, D., & Yang, Y. (2020). Active learning based on belief functions. SCIENCE CHINA-INFORMATION SCIENCES, 63(11).
[Google Scholar]
Smets, P., & Kennes, R. (1994). The transferable belief model. Artificial intelligence, 66(2), 191-234.
[Google Scholar]
Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis and machine intelligence, 24(7), 881-892.
[Google Scholar]
Zhu, Q., Cai, Y., & Liu, L. (1999). A global learning algorithm for a RBF network. Neural Networks, 12(3), 527-540.
[Google Scholar]
Asuncion, A., & Newman, D. (2007, November). UCI machine learning repository. Retrieved from https://archive.ics.uci.edu/ml
[Google Scholar]
Kaggle. (n.d.). Kaggle: Your machine learning and data science community. Retrieved from https://www.kaggle.com
[Google Scholar]
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[Google Scholar]

Article Metrics

Citations:

Google Scholar

Crossref

Scopus

Web of Science

Article Access Statistics:

PDF Downloads: 114

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

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

Email: [email protected]

Portico

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

Google Scholar

Crossref

Scopus

Web of Science

We use cookies