Weighted Evidence Combination Research Articles

An Improved Weighted Evidence Combination Based on Tangent Similarity and Its Application in Decision-Making

Dempster-Shafer theory (DST) has been widely recognized across multiple disciplines for its superior handling of uncertainty compared to traditional probability theory. Nonetheless, the application of Dempster's rule in the presence of conflicting evidence can lead to outcomes that are sometimes non-intuitive. To mitigate this issue, this paper proposes a new tangent similarity measure within DST to assess the conflict between evidences. The proposed measure adheres to several key properties, enhancing its ability to accurately reflect the similarity between evidences. Additionally, an improved weighted evidence combination framework utilizing the tangent similarity measure is developed. The effectiveness of the proposed method is demonstrated through a decision-making scenario in plant disease detection.

A novel weighted evidence combination method based on reliability and credibility degree with an application in decision making process

A novel weighted evidence combination method based on reliability and credibility degree with an application in decision making process

An evidential combination method with multi-color spaces for remote sensing image scene classification

Remote sensing image scene classification aims to commit the semantic labels according to the content of images. Convolutional Neural Network (CNN) is often used here to extract deep discriminative feature of remote sensing images for classification. In practice, CNN is usually trained by images in the Red Green Blue (RGB) color space. Whereas, CNN also can be trained by images in some other color spaces, e.g., Hue Saturation Value. The CNN models trained by images in diverse color spaces will perform differently because different color spaces often emphasize diverse color information. Thus, we present an Evidential Combination method with Multi-color Spaces (ECMS) to integrate the complementary information of different color spaces for classification performance improvement. In ECMS, labeled remote sensing images in the RGB color space are first converted into other color spaces, and then they are used to train CNN models, respectively. The soft classification results (of query images) yielded by these CNN models are combined by evidence theory. During fusion, the reliabilities/weights of these outputs of different CNN models are usually different, so they should not be equally treated for combination. In our approach, the weights are learnt by minimizing the mean squared error between the combination results and ground truth on labeled images. By doing this, weighted evidence combination of soft classification results is employed to make scene class decision. We conducted experiments on several datasets to verify the effectiveness of ECMS, and the results show ECMS can significantly improve classification accuracy compared with many existing methods.

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>

Research on driving behaviour assessment based on evidence theory in 5G‐VANET

With the rapid development of 5G and vehicular ad hoc network (VANET), the data fusion plays an increasingly important role in the driving behaviour assessment. However, the unreasonable distribution of conflicting evidence remains a significant issue for decision fusion based on evidence theory. By analysing the parameter information of driving behaviour recognition, this paper proposed a new weighted evidence combination approach to fuse the highly conflicting information. In short, the proposed combination method not only retains the excellent mathematical characteristics of Dempster's combination rule, but also fully considers the mutual relations between evidences and the influence of the characteristics of evidence body. Firstly, a new dissimilarity measure is put forward to quantify the conflict degree between the evidences. Furthermore, the uncertainty measure and internal conflict are adopted to determine the weight of each evidence. Based on the weighted averaging combination method, the reasonable combined results are obtained. According to the experimental results, the proposed method has better performance than the existing methods, and provides an effective solution for driving behaviour assessment.

Data Fusion for Target Recognition Based on Evidence Theory in IOT Environment

Data fusion using evidence theory in IOT applications has been used extensively to recoginze targets because it offers the advantage of handling uncertainty. But the traditional Dempster’s combination rule cannot deal with highly conflicting information because it often generates counter-intuitive results. In this paper, a new weighted evidence combination approach is proposed to solve this problem. First, two measures, i.e., an uncertainty measure of each evidence and a probabilistic-based dissimilarity measure between two evidences, are introduced to estimate the value of weight of each sensor. Then, when combining conflicting information, reasonable results can be produced by using weighted average of evidences and Dempster’s combination rule. Our experimental results showed that the proposed method has better performance in performance than the existing methods.

Combination of classifiers with incomplete frames of discernment

The methods for combining multiple classifiers based on belief functions require to work with a common and complete (closed) Frame of Discernment (FoD) on which the belief functions are defined before making their combination. This theoretical requirement is however difficult to satisfy in practice because some abnormal (or unknown) objects that do not belong to any pre-defined class of the FoD can appear in real classification applications. The classifiers learnt using different attributes information can provide complementary knowledge which is very useful for making the classification but they are usually based on different FoDs. In order to clearly identify the specific class of the abnormal objects, we propose a new method for combination of classifiers working with incomplete frames of discernment, named CCIF for short. This is a progressive detection method that select and add the detected abnormal objects to the training data set. Because one pattern can be considered as an abnormal object by one classifier and be committed to a specific class by another one, a weighted evidence combination method is proposed to fuse the classification results of multiple classifiers. This new method offers the advantage to make a refined classification of abnormal objects, and to improve the classification accuracy thanks to the complementarity of the classifiers. Some experimental results are given to validate the effectiveness of the proposed method using real data sets.

Safety Assessment of Dynamic Systems: An Evidential Group Interaction-Based Fusion Design

Considering the inherent unknown risks in the operation of complex dynamic system, it is very important to assess its safety accurately. Within the framework of human-in-the-loop, we propose a novel evidential group interaction-based (EGIB) safety assessment approach based on the evidence group interaction. With the analysis of safety assessment levels (SALs), the basic belief assignment (BBA) distribution of multiple safety indicators can be obtained. Fuzzy ranking technology is used to collect the individual’s cognition of these indicators. Using Shapley function in the framework of fuzzy measure and considering the cooperation effect, the peer importance is modeled. The collective group opinions of these indicators are obtained after the convergence process. The fusion procedure with weighted evidence combination is used to identify the safety state. To illustrate the effectiveness and practicability of the proposed method, we carry out simulation using real data obtained from electrical and observation communication system of deep-sea manned submersible, and comparsion is conducted between our approach and the baseline methods. Results show that the proposed approach can be applied to solve the safety assessment problems of complex dynamic systems in practice.

A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

The Dempster-Shafer (D-S) theory is widely applied in various fields involved with multi-sensor information fusion for radar target tracking, which offers a useful tool for decision-making. However, the application of D-S evidence theory has some limitations when evidences are conflicting. This paper proposed a new method combining the Pignistic probability distance and the Deng entropy to address the problem. First, the Pignistic probability distance is applied to measure the conflict degree of evidences. Then, the uncertain information is measured by introducing the Deng entropy. Finally, the evidence correction factor is calculated for modifying the bodies of evidence, and the Dempster’s combination rule is adopted for evidence fusion. Simulation experiments illustrate the effectiveness of the proposed method dealing with conflicting evidences.

A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

A novel weighted evidence combination rule based on improved entropy function with a diagnosis application

Managing conflict in Dempster–Shafer theory is a popular topic. In this article, we propose a novel weighted evidence combination rule based on improved entropy function. This newly proposed approach can be mainly divided into two steps. First, the initial weight will be determined on the basis of the distance of evidence. Then, this initial weight will be modified using improved entropy function. This new method converges faster when handling high conflicting evidences and greatly reduces uncertainty of decisions, which can be demonstrated by a numerical example where the belief degree is raised up to 0.9939 when five evidences are in conflict, an application in faulty diagnosis where belief degree is increased hugely from 0.8899 to 0.9416 when compared with our previous works, and a real-life medical diagnosis application where the uncertainty of decision is reduced to nearly 0 and the belief degree is raised up to 0.9989.

Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.

A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor Networks

Information fusion using evidence theory in wireless sensors networks has been used extensively to identify targets because it offers the advantage of handling uncertainty. But the classical Dempster’s combination rule cannot deal with highly conflicting information because it often generates counterintuitive results. In this paper, a new weighted evidence combination approach is proposed to solve this problem. First, two measures, i.e., a new contradiction measure of each body of evidence (BOE) and a probabilistic-based dissimilarity measure between two BOEs, are introduced to estimate the value of weight of each sensor. Then, when combining conflicting information, reasonable results can be produced by using weighted average of BOEs and Dempster’s rule. Our experimental results showed that the proposed method has better performance in convergence than the existing methods.

Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. The proposed approach is identified as two procedures. First, the weight is determined based on the distance of evidence. Then, the obtained weight value in the first step is modified by making advantage of Deng entropy function. Our proposed method can efficiently cope with high conflicting evidences with better performance of convergence. A numerical example is provided to demonstrate that the proposed method is reasonable and efficient in the end.

Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information

There is much interest in being able to combine crowdsourced data. One of the critical issues in information sciences is how to combine data or information that are discordant or inconsistent in some way. Many previous approaches have taken a majority rules approach under the assumption that most people are correct most of the time. This paper analyses crowdsourced land cover data generated by the Geo-Wiki initiative in order to infer the land cover present at locations on a 50 km grid. It compares four evidence combination approaches (Dempster-Shafer, Bayes, Fuzzy Sets and Possibility) applied under a geographically weighted kernel with the geographically weighted average approach applied in many current Geo-Wiki analyses. A geographically weighted approach uses a moving kernel under which local analyses are undertaken. The contribution (or salience) of each data point to the analysis is weighted by its distance to the kernel centre, reflecting Tobler's 1st law of geography. A series of analyses were undertaken using different kernel sizes (or bandwidths). Each of the geographically weighted evidence combination methods generated spatially distributed measures of belief in hypotheses associated with the presence of individual land cover classes at each location on the grid. These were compared with GlobCover, a global land cover product. The results from the geographically weighted average approach in general had higher correspondence with the reference data and this increased with bandwidth. However, for some classes other evidence combination approaches had higher correspondences possibly because of greater ambiguity over class conceptualisations and / or lower densities of crowdsourced data. The outputs also allowed the beliefs in each class to be mapped. The differences in the soft and the crisp maps are clearly associated with the logics of each evidence combination approach and of course the different questions that they ask of the data. The results show that discordant data can be combined (rather than being removed from analysis) and that data integrated in this way can be parameterised by different measures of belief uncertainty. The discussion highlights a number of critical areas for future research.

Weighted evidence combination based on improved conflict factor

The combination of heavy conflict evidence based on D-S evidence theory always has flaws. Research on a combination of conflict evidence at home and abroad is summarized and analyzed in detail, and the conclusion can be drawn that a modified evidence method of conflict evidence is more useful. Obtaining an effective measure of evidence conflict is the first step of conflict evidence combination. The existing conflict measure methods are summarized and the main problem of those methods is analyzed. An improved evidence conflict measure factor called Mconf is put forward based on previous research of conflict evidence combination. Mconf is mainly built up with modified evidence distance called mdBPA and combination conflict called k. A weighted evidence combination method based on Mconf is proposed. A typical case is used to validate the proposed method, and combination results show the proposed method is effective.

A Weighted Evidence Combination Method Based on Improved Conflict Measure Factor

D-S evidence theory is usually used for the fusion of multi-source information. But the fusion result is always against with general knowledge for the heavy conflict of evidence. Research on combination of conflict evidence at home and abroad is summarized and analyzed in detail. On the base of this, the conclusion that modified evidence combination method of conflict evidence is more useful can be dawn. Effective evidence conflict measure is the first step of conflict evidence combination. The existing conflict measure methods are summarized and the main problem of those methods is analyzed in detail. Based on previous research of conflict evidence combination, a modified measure factor of evidence conflict which is called Mconf is put forward. Mconf is mainly built up with modified distance of evidence named md BPA and traditional evidence conflict factor named k. The examples in this paper show that Mconf can measure the evidence conflict correctly, both for general evidence and conflict evidence. DOI: http://dx.doi.org/10.11591/telkomnika.v14i3.7850 Full Text: PDF

Operation Condition Monitoring using Temporal Weighted Dempster-Shafer Theory

System operation is a real time, dynamic decision process, a continuous observation should be implemented to support timely decision. Real time condition monitoring and diagnosis is featured with ongoing event sequence. The more recent observation, the much detailed, accurate information, and the more obsolete observations with much weak correlation to current faults and errors vise versa. Dempster-Shafer evidence theory is best suitable for the problem of redundant sensors, insufficient data reasoning. However, D-S base applications largely focused on causational relationship between symptoms and effects, and the fusion process of evidences was performed regardless whatever order observed. As an improvement to the frame of discernment of the D-S theory, we purposed a time weighted evidence combination method. Observed events were extracted from multiple time points to form a temporal evidence sequence. Basic probability assignment was altered by temporal weights in accordance with the time proximity between the observed events and current time. The temporal weights value set was in accordance with its occurring time point. Evidences with same timestamps should be allocated with the same temporal weights. An example was discussed to illustrate the temporal weight, D-S rule based assessment framework. In the framework, latest observed evidences stream were combined into the framework to improving fault recognition.

A Method of Fast Weighted Evidence Combination Exhibited by Compound Conflict

Aiming at the paradox of D-S evidence theory and computations exponential growth in dealing with large scale conflict evidence combination, a new weighted evidence combination method was proposed, which used conflict coefficient and evidence distance in order to measure the conflict. Through the analysis of single conflict representations weaknesses, compound conflict coefficient has been put forward, meanwhile, the evidence center and current center distance were defined, evidence weight was determined with current center distance and conflict coefficient. The experiment results show that the algorithm settles the paradox effectively, at the same time, computing speed has been greatly enhanced.

Multiple-feature Tracking Based on the Improved Dempster-Shafer Theory

Dempster-Shafer evidence theory is widely used in the fields of decision level information fusion. In order to overcome the problem of the counter-intuitive results encountered when using Dempster’s combination rule to combine the evidences which exist high conflict, a modified sequential weighted evidence combination is proposed. Firstly, the credibility of each evidence can obtained based on K-L distance, besides, the uncertainty of each evidence can obtained based on information entropy. Simultaneously, using the uncertainty of each evidence to improve the credibility of each evidence, then the weights of the bodies of evidence are obtained based on the improved credibility of each evidence, the weights generated are used to modify the bodies of evidence including the previous combination result, the previous evidence and the new arriving body of evidence at current step. Finally, according to the Dempster’s combination rule, the weighted average combination results can be obtained. In the experimental part, the improved method is used to fuse video multiple features in target tracking system and compared the results with the standard D-S theory. The simulation results show that the proposed method has better performance. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3364