Multi-Scale Metric Learning for Few-Shot Learning

Jiang, Wen; Huang, Kai; Geng, Jie; Deng, Xinyang

doi:10.1109/tcsvt.2020.2995754

Cited by 164 publications

(36 citation statements)

References 36 publications

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“…How to handle such kinds of uncertainty is still an open issue [ 53 , 54 ]. So far, various theories and their corresponding methods have been exploited, such as the extended fuzzy probability [ 55 ], soft sets [ 56 , 57 , 58 ], interval numbers [ 59 ], evidence theory [ 60 ], Z numbers [ 61 , 62 ], complex distributions [ 63 , 64 ], complex intuitionistic fuzzy sets [ 65 ], quantum-based [ 66 , 67 ], and others [ 68 , 69 ], as well as the consensus measure [ 70 ]. These methods were applied in various fields, like medical diagnosis [ 71 ] and decision-making [ 72 ].…”

Section: Preliminariesmentioning

confidence: 99%

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Xiao

2021

Sensors

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Multisource information fusion has received much attention in the past few decades, especially for the smart Internet of Things (IoT). Because of the impacts of devices, the external environment, and communication problems, the collected information may be uncertain, imprecise, or even conflicting. How to handle such kinds of uncertainty is still an open issue. Complex evidence theory (CET) is effective at disposing of uncertainty problems in the multisource information fusion of the IoT. In CET, however, how to measure the distance among complex basis belief assignments (CBBAs) to manage conflict is still an open issue, which is a benefit for improving the performance in the fusion process of the IoT. In this paper, therefore, a complex Pignistic transformation function is first proposed to transform the complex mass function; then, a generalized betting commitment-based distance (BCD) is proposed to measure the difference among CBBAs in CET. The proposed BCD is a generalized model to offer more capacity for measuring the difference among CBBAs. Additionally, other properties of the BCD are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. Besides, a basis algorithm and its weighted extension for multi-attribute decision-making are designed based on the newly defined BCD. Finally, these decision-making algorithms are applied to cope with the medical diagnosis problem under the smart IoT environment to reveal their effectiveness.

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Section: Preliminariesmentioning

confidence: 99%

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Xiao

2021

Sensors

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“…Entropy is often used in the analysis of fuzzy information, 44–47 complex networks, 48,49 and uncertainty measurement 50,51 . This paper uses a kind of belief entropy, called Deng entropy 52 to measure the uncertainty of an evaluation given by an expert.…”

Section: Preliminariesmentioning

confidence: 99%

Failure mode and effect analysis using multi‐linguistic terms and Dempster–Shafer evidence theory

Gou

Deng

et al. 2020

Quality & Reliability Eng

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Failure mode and effect analysis (FMEA) is a useful technique to identify and quantify potential failures. FMEA determines a potential failure mode by evaluating risk factors. In recent years, there are many works improving FMEA by allowing multiple experts to use linguistic term sets to evaluate risk factors. However, it is important to design a framework that can consider both the weight of risk factors and the weight of the experts. In addition, managing conflicts among experts is also an urgent problem to be addressed. In this paper, we proposed an FMEA model based on multi‐granularity linguistic terms and the Dempster–Shafer evidence theory. On the other hand, the weights for both experts and risk factors are taken into consideration. The weights are computed objectively and subjectively to ensure the reasonability. Further, we apply our method to an emergency department case, which shows the effectiveness of the method.

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“…Several novel fuzzy techniques and systems have been presented for reasoning with and managing uncertainty, including the extended intuitionistic fuzzy sets [9], rough sets [10], Z numbers [11], evidence theory [12,13], evidential reasoning [14], D numbers [15], R sets and numbers [16,17], and other hybrid methods [18]. These theories are applied broadly in various fields, such as image classification [19], medical diagnosis [20,21], information fusion [22], and decision making [23,24]. In these fuzziness-related approaches, one of the most useful tools to handle uncertainty is Dempster-Shafer evidence (DSE) theory [25,26], which has posed several attractive advantages: 1) quantitatively modeling uncertainty by means of a basic probability assignment (BBA) [27]; 2) the belief function is a type of fuzzy measure that provides partial information in terms of the appropriate fuzzy measure in relation to an uncertain variable [28,29]; 3) Dempster's combination rule (DCR) satisfies the commutative and associative laws [30,31]; 4) the results generated by the DCR have the characteristic of fault-tolerance and relieve the uncertainty level by the DCR [32,33].…”

Section: Introductionmentioning

confidence: 99%

A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis

Xiao

Cao

Jolfaei

2021

IEEE Trans. Fuzzy Syst.

126

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Dempster-Shafer evidence (DSE) theory, which allows combining pieces of evidence from different data sources to derive a degree of belief function that is a type of fuzzy measure, is a general framework for reasoning with uncertainty. In this framework, how to optimally manage the conflicts of multiple pieces of evidence in DSE remains an open issue to support decision making. The existing conflict measurement approaches can achieve acceptable outcomes but do not fully consider the optimization at the decision-making level using the novel measurement of conflicts. In this paper, we proposed a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making. Then, we investigated the properties of our proposed evidential correlation and conflict coefficients, which are all proven to satisfy the desirable properties for conflict measurement, including nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. We also presented several examples and comparisons to demonstrate the superiority of our proposed ECC method. Finally, we applied the proposed ECC in a decision-making application of motor rotor fault diagnosis, which verified the practicability and effectiveness of our proposed novel measurement.

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Multi-Scale Metric Learning for Few-Shot Learning

Cited by 164 publications

References 36 publications

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Failure mode and effect analysis using multi‐linguistic terms and Dempster–Shafer evidence theory

A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis

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