A belief Hellinger distance for D–S evidence theory and its application in pattern recognition

Song

et al. 2023

IEEE Access

The traditional Pignistic transformation is limited in the context of ''betting'', which faces information loss and is inconvenient for multi-source information fusion. To tackle this challenge, an Enhanced Pignistic transformation is proposed for the first time. New divergence and information volume measures are tailor-made for the enhanced Pignistic probability, and a novel information fusion algorithm is developed. To further prove the fusion algorithm's advantages in conflict management, it is applied in a new semi-automatic image segmentation scheme. Two uncertain decision-support techniques named adaptive belief assignment and scalable information extraction are raised, and a fuzzy heuristic refinement algorithm is conducted, fulfilling the gap between evidential decision-making and segmentation refinement. Experimental analysis shows the proposed segmentation algorithm is superior on four metrics each and can enhance the robustness of foreground segmentation, indicating the effectiveness of the proposal in solving the decision inaccuracy of evidential segmentation schemes.

Section: Background Knowledge Of D-s Evidence Theorymentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

Song

et al. 2023

IEEE Access

“…According to the measurement of uncertain information, many methods are used to identify the target and achieve the desired effect, such as evidential reasoning, 24 information fusion, 25 multidata fusion, 26,27 and so on. [28][29][30][31] Moreover, these methods have been widely used in various tasks, such as group decision-making (multiple attribute decision making-MADM), [32][33][34] reliability optimization, 35 medical system, 36,37 decision making, [38][39][40] fault diagnosis, [41][42][43] engineering system, 44 risk analysis, 45,46 and so on. [47][48][49][50] Regarding the processing of uncertain information, the most representative one is the D-S evidence theory.…”

Section: Introductionmentioning

confidence: 99%

Complex belief interval‐based distance measure with its application in pattern recognition

Int J of Intelligent Sys

Xiao

2022

Self Cite

The complex evidence theory is an effective methodology for multiattribute decision-making. Since difference measure between multiattribute plays an important role for conflict management in the process of multiattribute decision-making, how to measure discrepancy between complex basic belief assignments (CBBAs) in complex evidence theory is still an open issue. In this context, a new distance measurement (complex belief distance-CBD) is proposed in this paper by taking advantages of complex belief function and complex plausibility function, called complex belief interval-based distance. In addition, we compare the proposed CBD with the related work to illustrate its superiority. Next, based on CBD, we devise a novel multiattribute decision-making algorithm for pattern recognition. Finally, we apply the method to problems of medical diagnosis to verify the effectiveness of the proposed method.

“…Its theoretical basis lies in how to distinguish what is needed from different data. In the last decade, the application of pattern recognition technology in the field of machinery and equipment fault diagnosis has been common [1][2], with relevant improvements being propose. Along with the theoretical research on artificial neural networks, their application to pattern recognition techniques has resulted in a biomimetic pattern recognition method that shows excellent application prospects as a new method of information processing and fault diagnosis [3][4].…”

Section: Introductionmentioning

confidence: 99%

Aviation application of dual-weighted neural network based on biomimetic pattern recognition

2022

J. Phys.: Conf. Ser.

In order to solve the difficult problem of fault diagnosis in aviation equipment, this paper uses a biomimetic pattern recognition method. Compared to traditional pattern recognition, biomimetic pattern recognition is able to construct closed, complex geometries to cover all kinds of samples. Biomimetic pattern recognition is a “cognitive” approach, where the process between two similar things is considered asymptotic. It mathematically means that all similar samples in the feature space are continuously indistinguishable. The paper establishes a dual-weighted neural network model of an aircraft auxiliary power unit. Comparing the diagnostic process and results with traditional manual neural networks. The results show that biomimetic pattern recognition is a feasible and efficient diagnostic method for the fault diagnosis of aviation equipment. It provides a new way of thinking and method for the practical application of aviation equipment fault diagnosis.