Data Fusion Method Based on Improved D-S Evidence Theory

Zhang, Wei; Ji, Xilin; Yang, Yang; Chen, Jianwen; Gao, Zhipeng; Qiu, Xuesong

doi:10.1109/bigcomp.2018.00145

Cited by 18 publications

(10 citation statements)

References 17 publications

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“…They might be different POIs when they were measured by their name similarity, but they were matched POI pairs when measured by their address similarity. D-S evidence theory was an uncertain reasoning method that allowed people to model and reason about problems of inaccuracy and uncertainty, and allowed the whole problem and evidence to be decomposed into several sub-problems and sub evidence [35]. After dealing with the sub-problems and sub-evidence, correspondingly, the D-S combination rule was used to obtain the solution of the whole problem [36].…”

Section: The Improved D-s Evidence Theory Methodsmentioning

confidence: 99%

Point of Interest Matching between Different Geospatial Datasets

Deng

Luo

Liu

et al. 2019

IJGI

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Point of interest (POI) matching finds POI pairs that refer to the same real-world entity, which is the core issue in geospatial data integration. To address the low accuracy of geospatial entity matching using a single feature attribute, this study proposes a method that combines the D–S (Dempster–Shafer) evidence theory and a multiattribute matching strategy. During POI data preprocessing, this method calculates the spatial similarity, name similarity, address similarity, and category similarity between pairs from different geospatial datasets, using the multiattribute matching strategy. The similarity calculation results of these four types of feature attributes were used as independent evidence to construct the basic probability distribution. A multiattribute model was separately constructed using the improved combination rule of the D–S evidence theory, and a series of decision thresholds were set to give the final entity matching results. We tested our method with a dataset containing Baidu POIs and Gaode POIs from Beijing. The results showed the following—(1) the multiattribute matching model based on improved DS evidence theory had good performance in terms of precision, recall, and F1 for entity-matching from different datasets; (2) among all models, the model combining the spatial, name, and category (SNC) attributes obtained the best performance in the POI entity matching process; and (3) the method could effectively address the low precision of entity matching using a single feature attribute.

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Section: The Improved D-s Evidence Theory Methodsmentioning

confidence: 99%

Point of Interest Matching between Different Geospatial Datasets

Deng

Luo

Liu

et al. 2019

IJGI

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“…Due to the chemical differences, it tasted terrible. Today, because of food sciecne, there is a large amount of information about food chemicals [ 61 ] and how they make food taste. Consequently, Ahn et al [ 62 , 63 ] developed a flavor network of ingredients connected by shared flavor compounds, in which flavors were limited by the type of raw materials.…”

Section: Application Of Big Data In Food Industrymentioning

confidence: 99%

Application Research: Big Data in Food Industry

Ding

Wang³

et al. 2021

Foods

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A huge amount of data is being produced in the food industry, but the application of big data—regulatory, food enterprise, and food-related media data—is still in its infancy. Each data source has the potential to develop the food industry, and big data has broad application prospects in areas like social co-governance, exploit of consumption markets, quantitative production, new dishes, take-out services, precise nutrition and health management. However, there are urgent problems in technology, health and sustainable development that need to be solved to enable the application of big data to the food industry.

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“…The example data are from [30,[48][49][50], and these data from various sources are turned into BPAs, which are shown in Table 1. There are multiple pieces of evidence in each example.…”

Section: Conflict Evidence Fusionmentioning

confidence: 99%

An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021

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Dempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

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Data Fusion Method Based on Improved D-S Evidence Theory

Cited by 18 publications

References 17 publications

Point of Interest Matching between Different Geospatial Datasets

Point of Interest Matching between Different Geospatial Datasets

Application Research: Big Data in Food Industry

An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

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