Uncertainty Measurement of Basic Probability Assignment Integrity Based on Approximate Entropy in Evidence Theory
This work addresses uncertainty measurement for researchers in evidence theory, but it appears incremental as it builds on existing concepts like approximate entropy and network structures.
The paper tackled the problem of measuring uncertainty in the integrity of basic probability assignments (BPA) in evidence theory by proposing a method based on approximate entropy and network characteristics, resulting in a measure that helps identify the credibility of BPA.
Evidence theory is that the extension of probability can better deal with unknowns and inaccurate information. Uncertainty measurement plays a vital role in both evidence theory and probability theory. Approximate Entropy (ApEn) is proposed by Pincus to describe the irregularities of complex systems. The more irregular the time series, the greater the approximate entropy. The ApEn of the network represents the ability of a network to generate new nodes, or the possibility of undiscovered nodes. Through the association of network characteristics and basic probability assignment (BPA) , a measure of the uncertainty of BPA regarding completeness can be obtained. The main contribution of paper is to define the integrity of the basic probability assignment then the approximate entropy of the BPA is proposed to measure the uncertainty of the integrity of the BPA. The proposed method is based on the logical network structure to calculate the uncertainty of BPA in evidence theory. The uncertainty based on the proposed method represents the uncertainty of integrity of BPA and contributes to the identification of the credibility of BPA.