Process service quality evaluation based on Dempster-Shafer theory and support vector machine

Pei, Fengque; Li, Dongbo; Tong, Yifei; He, Fazhi

doi:10.1371/journal.pone.0189189

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…One of the aspects observed in the literature review was the precarious use of data analysis from the temporal and causal point of view, or even dependence between sources and sensors. (Pei et al 2017) observe the importance of analyzing time aspects in pre-processing. In (Zheng 2015), it is addressed that the probabilistic dependency-based data fusion method uses graph structure.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Causal Relations on Evidence Theory: an Application on Adverse Drug Reactions

Ribeiro

Garcia

Santos³

2021

FLAIRS

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The use of big data and information fusion in electronichealth records (EHR) allowed the identification of adversedrug reactions(ADR) through the integration of heteroge-neous sources such as clinical notes (CN), medication pre-scriptions, and pathological examinations. This heterogene-ity of data sources entails the need to address redundancy,conflict, and uncertainty caused by the high dimensionalitypresent in EHR. The use of multisensor information fusion(MSIF) presents an ideal scenario to deal with uncertainty,especially when adding resources of the theory of evidence,also called Dempster–Shafer Theory (DST). In that scenariothere is a challenge which is to specify the attribution of be-lief through the mass function, from the datasets, named basicprobability assignment (BPA). The objective of the presentwork is to create a form of BPA generation using analy-sis of data regarding causal and time relationships betweensources, entities and sensors, not only through correlation, butby causal inference.

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Section: Introductionmentioning

confidence: 99%

Temporal and Causal Relations on Evidence Theory: an Application on Adverse Drug Reactions

Ribeiro

Garcia

Santos³

2021

FLAIRS

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“…[ 13 ]. Much research and applications have been currently carried out on multi-classifier fusion [ 15 , 16 , 17 , 18 , 19 ] to fuse the outputs of these classifiers well, in which D-S evidence theory has been widely studied and applied. In the field of gait and motion pattern recognition, there are also some research and applications related to multiple classifier ensembles [ 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Application of Multi-Source Information Fusion in Multiple Gait Pattern Transition Recognition

Guo

Song

Liu

2022

Sensors

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Multi-source information fusion technology is a kind of information processing technology which comprehensively processes and utilizes multi-source uncertain information. It is an effective scheme to solve complex pattern recognition and improve classification performance. This study aims to improve the accuracy and robustness of exoskeleton gait pattern transition recognition in complex environments. Based on the theory of multi-source information fusion, this paper explored a multi-source information fusion model for exoskeleton gait pattern transition recognition in terms of two aspects of multi-source information fusion strategy and multi-classifier fusion. For eight common gait pattern transitions (between level and stair walking and between level and ramp walking), we proposed a hybrid fusion strategy of multi-source information at the feature level and decision level. We first selected an optimal feature subset through correlation feature extraction and feature selection algorithm, followed by the feature fusion through the classifier. We then studied the construction of a multi-classifier fusion model with a focus on the selection of base classifier and multi-classifier fusion algorithm. By analyzing the classification performance and robustness of the multi-classifier fusion model integrating multiple classifier combinations with a number of multi-classifier fusion algorithms, we finally constructed a multi-classifier fusion model based on D-S evidence theory and the combination of three SVM classifiers with different kernel functions (linear, RBF, polynomial). Such multi-source information fusion model improved the anti-interference and fault tolerance of the model through the hybrid fusion strategy of feature level and decision level and had higher accuracy and robustness in the gait pattern transition recognition, whose average recognition accuracy for eight gait pattern transitions reached 99.70%, which increased by 0.15% compared with the highest average recognition accuracy of the single classifier. Moreover, the average recognition accuracy in the absence of different feature data reached 97.47% with good robustness.

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“…In some other elds, the fusion information model is widely used 11 and shows good application. Pei et al 12 established a decision-level fusion method based on support vector machine (SVM) and Dempster-Shafer evidence theory to improve production quality. Although the effectiveness of the proposed method is demonstrated by a case, this method is established based on a large number of training data and has high computing complexity.…”

Section: Introductionmentioning

confidence: 99%

An online surface water COD measurement method based on multi-source spectral feature-level fusion

Guan

Tong

et al. 2019

RSC Adv.

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Process service quality evaluation based on Dempster-Shafer theory and support vector machine

Cited by 3 publications

References 21 publications

Temporal and Causal Relations on Evidence Theory: an Application on Adverse Drug Reactions

Temporal and Causal Relations on Evidence Theory: an Application on Adverse Drug Reactions

Research on the Application of Multi-Source Information Fusion in Multiple Gait Pattern Transition Recognition

An online surface water COD measurement method based on multi-source spectral feature-level fusion

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