A New Method to Determine Basic Probability Assignment Based on Interval Number

2019 ◽  
Author(s):  
Siqiang Zhong ◽  
Xingcheng Liu
Keyword(s):  
Interval Number ◽  
New Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability

scholarly journals An improved method to determine basic probability assignment with interval number and its application in classification

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882052 ◽  
Author(s):  
Bowen Qin ◽  
Fuyuan Xiao
Keyword(s):  
Evidence Theory ◽  
Classification Problem ◽  
Interval Number ◽  
Training Data ◽  
Cluster Method ◽  
Uncertain Information ◽  
Data Set ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability

Due to its efficiency to handle uncertain information, Dempster–Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probability assignment, which is the first step in evidence theory, is still an open issue. In this article, a new method integrating interval number theory and k-means++ cluster method is proposed to determine basic probability assignment. At first, k-means++ clustering method is used to calculate lower and upper bound values of interval number with training data. Then, the differentiation degree based on distance and similarity of interval number between the test sample and constructed models are defined to generate basic probability assignment. Finally, Dempster’s combination rule is used to combine multiple basic probability assignments to get the final basic probability assignment. The experiments on Iris data set that is widely used in classification problem illustrated that the proposed method is effective in determining basic probability assignment and classification problem, and the proposed method shows more accurate results in which the classification accuracy reaches 96.7%.

A new method to determine basic probability assignment using core samples

2014 ◽  
Vol 69 ◽  
pp. 140-149 ◽  
Author(s):  
Chenwei Zhang ◽  
Yong Hu ◽  
Felix T.S. Chan ◽  
Rehan Sadiq ◽  
Yong Deng
Keyword(s):  
New Method ◽  
Probability Assignment ◽  
Core Samples ◽  
Basic Probability Assignment ◽  
Basic Probability

Determination of Generalized Basic Probability Assignment Based on Interval Numbers

2014 ◽  
Vol 644-650 ◽  
pp. 934-938
Author(s):  
Rui Hong Wang ◽  
Pei Da Xu ◽  
Xin Chen ◽  
Yong Deng
Keyword(s):  
Fuzzy Theory ◽  
Evidence Theory ◽  
Interval Number ◽  
Interval Numbers ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Open World ◽  
Basic Probability

On the basis of the determination of basic probability assignment based on interval numbers, and combine the generalized evidence theory in the open world, the paper proposed an approach to determine generalized basic probability assignment based on the interval number, which offered a new idea of the determination of generalized basic probability besides the determination based on fuzzy theory. The rationality and effectiveness are verified by the experiments.

ON POSSIBILISTIC AND PROBABILISTIC APPROXIMATIONS OF UNRESTRICTED BELIEF FUNCTIONS BASED ON THE CONCEPT OF FUZZY T-PREORDER

2002 ◽  
Vol 10 (02) ◽  
pp. 185-200 ◽  
Author(s):  
ENRIC HERNANDEZ ◽  
JORDI RECASENS
Keyword(s):  
New Method ◽  
Belief Functions ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability

This paper presents a new method for approximating an unrestricted belief measure assuring that the "order" defined by the compatibility degree between evidence and the singletons set is preserved. Our approach, based on the concept of fuzzy T-preorder, also allows us to define several equivalence criteria over the set of all basic probability assignment functions on a given domain. Some others related aspects as uniqueness of the approximations are also addressed.

A new method to determine basic probability assignment from training data

2013 ◽  
Vol 46 ◽  
pp. 69-80 ◽  
Author(s):  
Peida Xu ◽  
Yong Deng ◽  
Xiaoyan Su ◽  
Sankaran Mahadevan
Keyword(s):  
Training Data ◽  
New Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability

scholarly journals Environmental Perception and Sensor Data Fusion for Unmanned Ground Vehicle

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yibing Zhao ◽  
Jining Li ◽  
Linhui Li ◽  
Mingheng Zhang ◽  
Lie Guo
Keyword(s):  
Data Fusion ◽  
Object Identification ◽  
New Method ◽  
Three Dimension ◽  
Chain Code ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability ◽  
Cross Country ◽  
Theory Of Evidence

Unmanned Ground Vehicles (UGVs) that can drive autonomously in cross-country environment have received a good deal of attention in recent years. They must have the ability to determine whether the current terrain is traversable or not by using onboard sensors. This paper explores new methods related to environment perception based on computer image processing, pattern recognition, multisensors data fusion, and multidisciplinary theory. Kalman filter is used for low-level fusion of physical level, thus using the D-S evidence theory for high-level data fusion. Probability Test and Gaussian Mixture Model are proposed to obtain the traversable region in the forward-facing camera view for UGV. One feature set including color and texture information is extracted from areas of interest and combined with a classifier approach to resolve two types of terrain (traversable or not). Also, three-dimension data are employed; the feature set contains components such as distance contrast of three-dimension data, edge chain-code curvature of camera image, and covariance matrix based on the principal component method. This paper puts forward one new method that is suitable for distributing basic probability assignment (BPA), based on which D-S theory of evidence is employed to integrate sensors information and recognize the obstacle. The subordination obtained by using the fuzzy interpolation is applied to calculate the basic probability assignment. It is supposed that the subordination is equal to correlation coefficient in the formula. More accurate results of object identification are achieved by using the D-S theory of evidence. Control on motion behavior or autonomous navigation for UGV is based on the method, which is necessary for UGV high speed driving in cross-country environment. The experiment results have demonstrated the viability of the new method.

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

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–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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