Dempster-Shafer Evidence Research Articles

Attribute reduction based on D-S evidence theory in a hybrid information system

As an important uncertainty reasoning method, Dempster-Shafer (D-S) evidence theory has been widely applied to expert system, comprehensive evaluation, information fusion and decision analysis. However, it has not been fully valued in uncertainty measurement. A hybrid information system (HIS) means an information system that contains many types of attributes (e.g., categorical attribute, real-valued attribute and attribute with missing values, etc.). It is more difficult to measure an HIS than an ordinary IS. This paper studies the use of evidence theory to measure the uncertainty of an HIS. Firstly, a novel distance between two objects in an HIS considering decision attributes is constructed, and secondly, the tolerance relation based on the constructed distance is established in an HIS. And then, belief and plausibility functions are defined by the tolerance relation. Furthermore, several algorithms for attribute reduction are designed on the basis of the defined belief and plausibility functions. In addition, we come to a series of conclusions on the relation among θ-reduction by using decision attributes, θ-belief reduction and θ-plausibility reduction, which further confirms the effectiveness of the designed attribute reduction algorithms. Finally, the experimental results and statistical test show that the defined belief and plausibility functions work well in measuring the uncertainty of an HIS and the designed reduction algorithm is superior to several state-of-the-art algorithms in classification accuracy. These results will provide a wider perspective on the uncertainty of an HIS.

A multi-granularity information fusion method based on logistic regression model and Dempster-Shafer evidence theory and its application

A multi-granularity information fusion method based on logistic regression model and Dempster-Shafer evidence theory and its application

A new complex evidence theory

Dempster-Shafer evidence theory is widely used in the field of information fusion since it satisfies weaker conditions than probability theory. Nevertheless, the description space of the current evidence theory is only real space, and it cannot effectively describe and process the uncertain information in the face of multidimensional characteristic data and periodic data with phase angle changes. Thus, in this paper, evidence theory is extended to the complex Dempster-Shafer evidence theory. The mass function that is used to describe the uncertain information extends from the real space to the complex space, named as complex mass function. The modulus of the complex mass function indicates the degree of support for the proposition. Moreover, other basic concepts that are used to describe uncertainty information are also defined and discussed. To perfect the complex evidence theory, the complex Dempster rule of combination is supplemented. The complex Dempster rule of combination is an extension of Dempster rule of combination, which satisfies the commutative and associative laws just as Dempster rule of combination does, and it can degenerate into Dempster rule of combination. This paper also proposes a method to generate complex mass function and apply it to target recognition. The recognized results show that compared with the mass function, the target recognition rate is larger by using the complex mass function.

Cerebrovascular segmentation in phase-contrast magnetic resonance angiography by multi-feature fusion and vessel completion.

Phase-Contrast Magnetic Resonance Angiography (PC-MRA) is a potential way of cerebrovascular imaging, which can suppress non-vascular tissue while presenting vessels. But PC-MRA will bring much noise and is easy to result in partially broken vessels. Usually, deep learning is an effective way to quantify vessels. However, how to choose an appropriate deep learning model is an important and difficult issue. In this work, we adopted the Dempster-Shafer (DS) evidence theory to fuse multi-feature from different models. Also, the vessel thinning and completion method were proposed to fill in information of broken cerebrovascular in PC-MRA images. For quantitative analysis, we chose Precision (PRE), Recall (REC), and Dice Similarity Coefficient (DSC) as assessment metrics, and established U-Net, V-Net, and Dense-Net. The 22 subjects tested this method. Comparison with different fusion strategies and common deep learning models have confirmed the effectiveness of the proposed method. In addition, we scanned Contrast-Enhanced MRA (CE-MRA) for 12 patients to verify reliability of vessel completion. Experiments show that the completion vessel can improve the matching ratio with CE-MRA, which has clinical potential.

Road extraction in vague images on gray scale consistency and improved MSR and D-S evidence

Road detection on aerial and remote sensing vague images is a hard task. In this paper, an automatic road detection method for the vague images is proposed. The method firstly uses an improved MSR algorithm to enhance image, and it automatically takes different scales in different image regions, based on the image depths obtained by the dark channel prior algorithm. Then the enhanced image is roughly segmented on the principle of the local gray scale consistency, in that, an eight-neighborhood template is considered as a processing unit in which a threshold is utilized for all the neighboring pixels of the detecting pixel. Finally, the Dempster-Shafer (D-S) evidence for road features is applied to finalize road tracing in the binary image, where, the road features include length, width, aspect ratio and fullness rate, all the parameters are obtained in the least external rectangle of a road segment, and then the detected roads are regulated. In experiments, 300 vague road images were selected for testing, by comparing to several traditional algorithms and recent semantic methods, the testing results show that the new method is satisfactory, and the detection accuracy is up to 89%.

An MFD Construction Method Considering Multi-Source Data Reliability for Urban Road Networks

Road network traffic management and control are the key mechanisms to alleviate urban traffic congestion. With this study, we aimed to characterize the traffic flow state of urban road networks using the Macroscopic Fundamental Diagram (MFD) to support area traffic control. The core property of an MFD is that the network flow is maximized when network traffic stays at an optimal accumulation state. The property can be used to optimize the temporal and spatial distribution of traffic flow with applications such as gating control. MFD construction is the basis of these MFD-based applications. Although many studies have been conducted to construct MFDs, few studies are dedicated to improving the accuracy considering the reliability of different sources of data. To this end, we propose an MFD construction method using multi-source data based on Dempster–Shafer evidence (DS evidence) theory considering the reliability of different data sources. First, the MFD was constructed using VTD and CSD, separately. Then, the fused MFD was derived by quantifying the reliability of different sources of data for each MFD parameter based on DS evidence theory. The results under real data and simulated data show that the accuracy of the constructed MFDs was greatly improved considering the reliability of different data sources (the maximum MFD estimation error was reduced by 22.3%). The proposed method has the potential to support the evaluation of traffic operations and the optimization of signal control schemes for urban traffic networks.

A generalized Rényi divergence for multi-source information fusion with its application in EEG data analysis

The application of multisource information fusion in real-world scenarios is an emerging practice because it effectively uses consistent and complementary data to optimize decision-making. Dempster-Shafer (D-S) evidence theory is prevalent because it competently handles uncertainty problems by assigning basic probability assignments (BPAs) to multielement subsets. However, a counterintuitive result may be obtained when the evidence is highly conflicting. To overcome this flaw, this paper defines a new divergence measurement to quantify the differences between BPAs; we name this new metric the belief Rényi divergence. The belief Rényi divergence takes the number of possible hypotheses into consideration, which makes it a more rational and effective difference measurement in the realm of evidence theory. Additionally, some important properties of the belief Rényi divergence are extensively explored and proven, in which the belief Rényi divergence also connects to Kullback-Leibler divergence, Hellinger distance and χ2 divergence. Moreover, a novel multisource information fusion method is devised based on the proposed belief Rényi divergence and belief entropy. Our proposed belief Rényi divergence can efficiently model the differences between evidence, and the belief entropy is used to calculate the information volume of evidence. Thus, the proposed method can sufficiently exploit the relationships among evidence and the information volume of the evidence itself. Two case studies are illustrated to verify the effectiveness and practicality of the proposed method. Also, an experiment on an iris dataset classification is presented to verify the performance of the proposed method. In addition, an EEG data analysis application demonstrates that the proposed method can be effectively used in real-world applications.

A Weighted Evidence Combination Method for Multisensor Data Fusion

<p>Multisensor information fusion exerts a key part in lots of practical usages. Dempster-shafer evidence theory has drawn extensive attention in many scopes of information fusion due to its flexibility and effectiveness in dealing with uncertain data without aforehand data. But when combining highly contradictory evidence with Dempster’s combinatorial principles, it can result in counterintuitive results. To solve the issue, the study proposes a multi-sensor data weighted evidence combination fusion method based on inter-evidence difference measure. Firstly, different measures including evidence distance and conflict are with the definition of characterizing distinctions between the two pieces of evidence. Then, according to the difference between each evidence and the average evidence, the weight coefficients of each evidence are calculated. In the end, initial evidence is discounted according to weighting factor, as well as Dempster’s combination principle is adopted to discount the evidence for fusion. Many instances show that this way can efficiently treat highly conflicting evidence and has good convergence performance.</p> <p> </p>

A new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios

Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811.

Dempster-shafer theory and linguistic intuitionistic fuzzy number-based framework for blending knowledge from knowledge repositories: An approach for knowledge management

Based on Dempster-Shafer Evidence Theory (DSET) and Linguistic Intuitionistic Fuzzy Numbers (LIFN), this study presents a paradigm for Knowledge Blending (KB) that supports the Knowledge Management (KM) process. The KB framework highlights and analyzes knowledge blending, the emergence of new insights, the ordering of knowledge repositories, and selected knowledge entities that are part of such a knowledge combination. The KB method enables the analysis of knowledge repositories using linguistic terms and quantitative variables and the identification of the ordering of knowledge repositories and specific entities. Ordering knowledge repositories and entities during KB is tricky since knowledge providers employ a range of adjectives, rankings, interpretations, and linguistic terms, rendering the KB process ambiguous. To address the issue, we leverage DSET to describe the LIFNs extracted from knowledge sources as Basic Probability Assignments (BPA). Then, we merge subjective and objective weights obtained from knowledge repositories. To amend evidence from knowledge repositories, we apply the combined weight. Finally, the comprehensive evaluation value of each option is quantified using the combination rule of evidence, which aids in the blending of knowledge repositories and entities acquired from the knowledge repositories. We discuss a case study to explain the proposed method and compare it to other approaches in order to demonstrate its viability and usefulness. The case study highlights the value of KM during the knowledge structuring and auditing stages of the KM process.

Transformer Fault Diagnosis Method Based on Neural Network and D-S Evidence Theory

Abstract The BP neural network is used to calculate the basic fault probability distribution of the dissolved gas in the transformer oil and the core grounding online monitoring data, and the Dempster-Shafer evidence theory is used to fuse the multi-source information of the basic probability of various types of faults to obtain a transformer fault diagnosis model. Transformer samples are used to verify the model, and the support vector machine and convolutional neural network fault diagnosis models are compared, and it is concluded that the proposed method is better in terms of fault diagnosis accuracy and stability.

Hybrid intrusion detection system based on Dempster-Shafer evidence theory

Cyber-attacks are becoming increasingly sophisticated, posing greater challenges in accurately detecting intrusions. Failure to prevent intrusions could degrade the credibility of security services. Intrusion Detection System (IDS) is one of the most effective paradigms to identify attack behaviors. This paper proposes a novel hybrid intrusion detection system called DST-IDS. The proposed method employs both packet-based and flow-based intrusion detection techniques and combines them with Dempster-Shafer Theory (DST). DST-IDS has an ensemble-like framework. It takes both traffic flows and their first N packets as inputs; flow-based IDS aims to predict traffic flows and packet-based IDS detects attacks in the corresponding packets; DST is then applied to fuse predictions of flow-based IDS and packet-based IDS to a final detection result. We also design a novel data collection/processing tool in DST-IDS to reduce the data volume required to perform intrusion detection and enable early detection. In addition, DST-IDS is designed to work with heterogeneous data distribution where the distribution of the training dataset can differ from the data distribution during implementation. This property drastically improves the practicality of DST-IDS. We run experiments on public datasets and real networks to evaluate the proposed method. The experimental results show that DST-IDS outperforms state-of-the-art benchmarks in terms of intrusion detection accuracy and detection speed. Particularly, DST-IDS provides real-time detection in real networks and handles well heterogeneous data distribution.

Cutting-state identification of machine tools based on improved Dempster-Shafer evidence theory

The reliability of machine tools is highly influenced by the cutting state. The traditional recognition method of cutting state is emphasized on a single classifier, which has the weakness of low identification accuracy and strong randomness. This paper proposes a cutting-state identification method based on improved Dempster-Shafer (DS) evidence theory. This method is divided into multi-classifier preliminary-diagnosis layer and improved DS information-fusion layer. The wavelet packet analysis method is extracted as the input of multi-classifier (Back Propagation (BP) neural network, genetic algorithm (GA) optimized BP neural network and thinking evolution (mind evolutionary algorithms) MEA optimized BP neural network). After the preliminary judgment, the improved DS information-fusion method is integrated as the final judgment, and finally, the effectiveness and feasibility of the method are verified.

A novel multi-information decision fusion based on improved random forests in HVCB fault detection application

Evaluating the mechanical state of high-voltage circuit breakers (HVCBs) based on vibration information has currently become an important research direction. In contrast to the unicity of the travel–time and current–time curves, the vibration information from the different positions is diverse. These differences are often overlooked in HVCB fault identification applications. Additionally, the fault recognition results based on different location information often vary, and conflicting diagnosis results directly cause the accurate identification of the fault type to fail. Therefore, in this paper, a novel multi-information decision fusion approach is proposed based on the improved random forest (RF) and Dempster-Shafer evidence theory. In the proposed method, the diagnostic distribution of all classification regression trees in the RF is considered to solve the conflicts among the multi-information diagnosis results. Experimental results show that the proposed method eases the contradiction of multi-position diagnostic results and improves the accuracy of fault identification. Furthermore, compared to the common classifiers and probability generation methods, the effectiveness and superiority of the proposed method are verified.

A Methodology to Generate Integrated Land Cover Data for Land Surface Model by Improving Dempster-Shafer Theory

Land cover type is a key parameter for simulating surface processes in many land surface models (LSMs). Currently, the widely used global remote sensing land cover products cannot meet the requirements of LSMs for classification systems, physical definition, data accuracy, and space-time resolution. Here, a new fusion method was proposed to generate land cover data for LSMs by fusing multi-source remote sensing land cover data, which was based on improving Dempster-Shafer evidence theory with mathematical models and knowledge rules optimization. The new method has the ability to deal with seriously disagreement information, thereby improving the robustness of the theory. The results showed the new method can reduce the disagreement between input data and realized the conversion of multiple land cover classification systems to into a single land cover classification system. China Fusion Land Cover data (CFLC) in 2015 generated by the new method maintained the classification accuracy of the China land use map (CNLULC), which is based on visual image interpretation and further enriched land cover classes of input data. Compared with Geo-Wiki observations in 2015, the overall accuracy for CFLC is higher than other two global land cover data. Compared with the observations, the 0–10 cm soil moisture simulated by the CFLC in Noah–MP LSM during the growing season in 2014 had better performance than that simulated by initial land cover data and MODIS land cover data. Our new method is highly portable and generalizable to generate higher quality land cover data with a specific land cover classification system for LSMs by fusing multiple land cover data, providing a new approach to land cover mapping for LSMs.

A hybrid prediction frame for HEAs based on empirical knowledge and machine learning

Phase formation plays key role in the properties of high-entropy alloys (HEAs). If the phases of HEAs can be accurately predicted, the number of experiments can be greatly reduced, and the process of material design can be greatly accelerated. Machine-learning methods have been successfully and widely applied to predict the phases of HEAs. However, the accuracy of a single machine-learning (ML) algorithm is not ideal and different ML algorithms may predict different results. These issues hinder the application of ML in material design. In this paper, a hybrid frame for HEAs phase prediction, which combines machine-learning and empirical knowledge, is proposed. First, for the purpose of solving the problem that a sample may be predicted as inconsistent prediction phases by different algorithms, the Dempster-Shafer (DS) evidence theory is adopted to fuse the inconsistent of the predicted phases among different algorithms, and provide a fusion prediction phase with the highest credibility. Second, a conflict-resolution model with high accuracy based on the improved DS evidence theory is proposed. Last, the empirical knowledge criterion is combined with the conflict-resolution model to improve the efficiency and accuracy of the hybrid prediction frame. The 426 different HEAs samples consisting of 180 quinaries, 189 senaries, and 57 septenaries were collected to validate against the effectiveness of the proposed methods. The experimental results demonstrate the hybrid prediction frame achieves higher accuracy and better performance than single ML algorithm. Keywords: Hybrid model; High-entropy alloys; Phase prediction; DS evidence theory

Serious Damage Localization of Continuous Girder Bridge by Support Reaction Influence Lines

A novel damage detection approach is proposed in this study for a continuous girder bridge in which support reaction influence lines (ILs) are adopted. First, the relationship between the local damage of a continuous girder bridge and a damage index, based on support reaction ILs, is established through analytical derivation. Subsequently, the sensitivity of a support reaction IL-based damage index is analyzed using Dempster-Shafer (D-S) evidence theory, and it shows that the support reaction IL-based damage index is more noise-resistant if more support reaction ILs from a variety of locations are used. Three case studies (a simple numerical study of a two-span continuous beam, a laboratory experimental study of a two-span aluminum beam, and a complicated numerical study of a continuous girder bridge in Xiamen) have been conducted to validate the effectiveness of the proposed method in different damage scenarios, including single damage and multiple damages. Satisfactory damage identification results can be obtained even in high-level measurement noise conditions, showing that the proposed approach offers a promising field detection technique for identifying local structural damages in continuous girder bridges.

Conflicting evidence combination based on Belief Mover’s Distance

The Dempster-Shafer evidence theory has been extensively used in various applications of information fusion owing to its capability in dealing with uncertain modeling and reasoning. However, when meeting highly conflicting evidence, the classical Dempster’s combination rule may give counter-intuitive results. To address this issue, we propose a new method in this work to fuse conflicting evidence. Firstly, a new evidence distance metric, named Belief Mover’s Distance, which is inspired by the Earth Mover’s Distance, is defined to measure the difference between two pieces of evidence. Subsequently, the credibility weight and distance weight of each piece of evidence are computed according to the Belief Mover’s Distance. Then, the final weight of each piece of evidence is generated by unifying these two weights. Finally, the classical Dempster’s rule is employed to fuse the weighted average evidence. Several examples and applications are presented to analyze the performance of the proposed method. Experimental results manifest that the proposed method is remarkably effective in comparison with other methods.

A Dempster-Shafer evidence theory for environmental risk assessment in failure modes and effects analysis of Oil and Gas Exploitation Plant

The oil, gas, and petrochemical industries, as one of the largest sources of environmental pollutants, have different types and levels of pollution depending on the type of input materials, process steps, and output products. Various stages of exploration, extraction and processing of oil and gas have many environmental effects, such as those on soil, air, water, creatures, plants, and even humans. In this paper, a failure mode and effects analysis (FMEA) is employed to identify failures and environmental risks in an oil and gas exploitation plant. Dempster–Shafer (DS) theory of evidence is then proposed for environmental risk assessment due to its effectiveness in dealing with uncertain and subjective information. The assesments of experts and their confidence levels of their responses are employed to construct the basic probability assignments (BPA) in DS theory of evidence. Furthermore, a new weighting method is proposed to obtain the discounted BPA which reduces the uncertainty in the information sources and improves the quality of information before combining different sources of information. Finally, the proposed method is applied to an oil and gas exploitation plant to assess environmental risks.

A survey on road hazardous material transportation accidents in Iran

Hazardous material road transportation is one of the most challenging procedures performed by large trucks and trailers. In this study, after examining and analyzing road hazardous material transportation accidents, occurred over 5 years in Iran, the contributing factors of road hazardous material transportation accidents were determined. Subsequently, the introduced factors were prioritized using fault tree analysis and the Dempster-Shafer evidence theory. The results revealed that the frequency of accidents has significantly increased in recent years. It is shown that the three pivotal factors in road hazardous material transportation accidents were transport vehicle, packaging and loading of hazardous materials, and human factors. These findings provide an empirically supported theoretical basis for transportation corporations to take corrective and preventative measures to reduce the accident risks. A novel technique has been introduced for analyzing the causes of road hazardous material transportation accidents. Finally, the absence of hazardous material transportation companies in Iran is introduced as a critical reason for the higher frequency of such accidents in Iran compared to other countries.