TECM: Transfer learning-based evidential c-means clustering

Jiao, Lianmeng; Wang, Rui; Liu, Zhun-ga

doi:10.1016/j.knosys.2022.109937

Cited by 21 publications

(8 citation statements)

References 57 publications

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“…Dempster-Shafer evidence theory is a typical mathematical tool for information fusion 18,19 . It has been extended as well as been applied in many practical fields such as intelligent decision-making [20][21][22][23] , classification [24][25][26] , clustering [27][28][29] , supplier selection 30 , and risk analysis 31,32 . Some new theories are proposed for uncertain information modelling and processing based on Dempster-Shafer evidence theory, for example, the transferable belief model 33 , the belief functions 34 , the evidential reasoning theory 35 , the Dezert-Smarandache theory 36 , D Numbers 37 , the belief rule-base model 38,39 , and the complex mass function 40 .…”

Section: Situation Assessment In Air Combat Considering Incomplete Fr...mentioning

confidence: 99%

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Zhou

Tang

Zhao

2022

Sci Rep

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For situation assessment in air combat, there may be incomplete information because of new technologies and unknown or uncertain targets and threats. In this paper, an improved method of situation assessment for air combat environment considering incomplete frame of discernment in the evidence theory is proposed to get a more accurate fusion result for decision making in the battlefield environment. First, the situation in air combat is assessed with knowledge. Then, the incomplete frame of discernment in the generalized evidence theory, which is an extension of Dempster–Shafer evidence theory, is adopted to model the incomplete and unknown situation assessment. After that, the generalized combination rule in the generalized evidence theory is adopted for fusion of situations in intelligent air combat. Finally, real-time decision-making in situation assessment can be reached for actions to take. Experiments in situation assessment of air combat with incomplete and uncertain situations show the rationality and effectiveness of the proposed method.

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Section: Situation Assessment In Air Combat Considering Incomplete Fr...mentioning

confidence: 99%

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Zhou

Tang

Zhao

2022

Sci Rep

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“…Except for the theoretical justifications described above, the evidence theory has also been intensively studied in various real-world applications [24], [25]. However, only a little amount of literature relates to evidential semi-automatic image segmentation [26].…”

Section: Introduction and Related Workmentioning

confidence: 99%

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

Zhang

Song

et al. 2023

IEEE Access

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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.

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“…Bayesian probability theory, Dempster–Shafer evidence theory (D-S evidence theory), fuzzy structure and some other methods [ 5 , 6 , 7 ] have been used to enhance the ability to process uncertain data [ 8 , 9 , 10 ]. Among them, the Dempster–Shafer theory has an outstanding performance in uncertain information modeling and fusion in practical applications, such as multi-sensor target recognition [ 11 ], industrial alarm system design [ 12 ], web news extraction [ 13 ] classification with uncertainty [ 14 , 15 , 16 ], clustering [ 17 , 18 , 19 ] and so on [ 20 , 21 ]. The research on the Brain–Computer Interface also expanded the application of D-S evidence theory in the medical field [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster–Shafer Evidence Theory for Uncertain Information Fusion

Tang

Zhou

et al. 2023

Entropy

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Dempster–Shafer evidence theory is widely used to deal with uncertain information by evidence modeling and evidence reasoning. However, if there is a high contradiction between different pieces of evidence, the Dempster combination rule may give a fusion result that violates the intuitive result. Many methods have been proposed to solve conflict evidence fusion, and it is still an open issue. This paper proposes a new reliability coefficient using betting commitment evidence distance in Dempster–Shafer evidence theory for conflict and uncertain information fusion. The single belief function for belief assignment in the initial frame of discernment is defined. After evidence preprocessing with the proposed reliability coefficient and single belief function, the evidence fusion result can be calculated with the Dempster combination rule. To evaluate the effectiveness of the proposed uncertainty measure, a new method of uncertain information fusion based on the new evidence reliability coefficient is proposed. The experimental results on UCI machine learning data sets show the availability and effectiveness of the new reliability coefficient for uncertain information processing.

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TECM: Transfer learning-based evidential c-means clustering

Cited by 21 publications

References 57 publications

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

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

A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster–Shafer Evidence Theory for Uncertain Information Fusion

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