Abstract
Conflicting evidence affects the final target recognition results. Thus, managing conflicting evidence efficiently can help to improve the belief degree of the true target. In current research, the existing approaches based on belief entropy use belief entropy itself to measure evidence conflict. However, it is not convincing to characterize the evidence conflict only through belief entropy itself. To solve this problem, we comprehensively consider the influences of the belief entropy itself and mutual belief entropy on conflict measurement, and propose a novel approach based on an improved belief entropy and entropy distance. The improved belief entropy based on pignistic probability transformation function is named pignistic probability transformation (PPT) entropy that measures the conflict between evidences from the perspective of self-belief entropy. Compared with the state-of-the-art belief entropy, it can measure the uncertainty of evidence more accurately, and make full use of the intersection information of evidence to estimate the degree of evidence conflict more reasonably. Entropy distance is a new distance measurement method and is used to measure the conflict between evidences from the perspective of mutual belief entropy. Two measures are mutually complementary in a sense. The results of numerical examples and target recognition applications demonstrate that our proposed approach has a faster convergence speed, and a higher belief degree of the true target compared with the existing methods.
Highlights
Information is affected by various subjective factors and objective environment, and there is some uncertainty
Compared with the state-of-the-art belief entropy [27,30,31,32,33,34], it can measure the uncertainty of evidence more accurately, and make full use of the intersection information of evidence to better estimate the degree of evidence conflict
Two numerical examples in experiments are illustrated that the novel method is feasible and superior in dealing with the conflicting evidence, where the belief degree of the correct hypothesis has 3.8% and 1.6% increase compared to existing methods, respectively
Summary
Information is affected by various subjective factors and objective environment, and there is some uncertainty. Tao et al propose a modified average method to combine conflicting evidence based on belief entropy and induced ordered weighted averaging operator [25]. We propose PPT entropy based on pignistic probability transformation function It fully considers the influence of the intersection between propositions on uncertainty and makes the uncertainty measurement of evidence more accurate and range wider. A novel method for conflict management is presented based on PPT entropy and entropy distance. It measures conflict between evidences from the perspective of belief entropy itself and mutual belief entropy.
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