Combination of classifiers with incomplete frames of discernment

Liu, Zhun-ga; Duan, Jingfei; Huang, Linqing; Dezert, Jean; Zhao, Yongqiang

doi:10.1016/j.cja.2021.04.020

Cited by 13 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evidence theory was first proposed and developed by Dempster and Shafer, which is also called Dempster-Shafer theory. The Frame of Discernment (FD) is the most fundamental concept in evidence theory [11] which is the set of all possible results that can be recognized for a particular decision problem. The FD is similar to the sample of random variables in probability theory, which consists of a finite number of essential elements.…”

Section: Basic Principles Of Evidence Theorymentioning

confidence: 99%

Structural Interval Reliability Algorithm Based on Bernstein Polynomials and燛vidence Theory

Zhang¹,

Ni²,

Hu³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors. Traditional structural reliability analysis methods often convert the limit state function to the polynomial form to measure whether the structure is invalid. The uncertain parameters mainly exist in the form of intervals. This method requires a lot of calculation and is often difficult to achieve efficiently. In order to solve this problem, this paper proposes an interval variable multivariate polynomial algorithm based on Bernstein polynomials and evidence theory to solve the structural reliability problem with cognitive uncertainty. Based on the non-probabilistic reliability index method, the extreme value of the limit state function is obtained using the properties of Bernstein polynomials, thus avoiding the need for a lot of sampling to solve the reliability analysis problem. The method is applied to numerical examples and engineering applications such as experiments, and the results show that the method has higher computational efficiency and accuracy than the traditional linear approximation method, especially for some reliability problems with higher nonlinearity. Moreover, this method can effectively improve the reliability of results and reduce the cost of calculation in practical engineering problems.

show abstract

Section: Basic Principles Of Evidence Theorymentioning

confidence: 99%

Structural Interval Reliability Algorithm Based on Bernstein Polynomials and燛vidence Theory

Zhang¹,

Ni²,

Hu³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…DS rule generally produces good performance in normal case, but it may yield unreasonable result when the BBA's has high conflict. Many modified combination rules [42], [43], [44], [45], [46], [47], [48] have been developed to deal with the conflicting cases.…”

Section: Basics Of Belief Functionsmentioning

confidence: 99%

Evidence Combination for Pattern Classification With Missing Data

Lizhuo

2023

Preprint

View full text Add to dashboard Cite

It remains a challenging problem to classify the incomplete patterns with randomly missing values. In some applications, it is difficult for us to collect complete attributes of target due to the complex sensing environment, and the observed patterns are all with more or less missing values. In order to well classify such incomplete patterns, we propose a new classification method based on evidence combination. Because the patterns are all considered with missing attribute values, it is hard to accurately estimate the missing values of object to classify. We propose to classify the object using each available attribute respectively. The K-nearest neighbors (K-NN) of object are found in training data space according to each attribute, and K Basic belief assignments (BBA) are constructed corresponding to the K-NN. The BBA reflects the degree of object belonging to each class. Then a two step evidence combination strategy is developed for combining the K BBA's. The BBA's associated with the same class are combined by classical DS rule at first. When the K-NN belong to different classes, the previous DS combination results associated with different classes may highly conflict, and they are further combined by PCR5 rule, which can well manage the high conflict via proper conflicting masses redistribution. We can classify the object using other attribute one by one in the similar way. When multiple classification results corresponding to different attributes are collected, a weighted evidence combination method is employed to fuse these results. The weighting factors of the results are optimized by minimizing an error criterion. The object is finally classified depending on this combination result. Experimental results with various data sets show the effectiveness of the proposed method comparing with other related methods.

show abstract