Abstract
How to evaluate the reliability of the information sources is a significant and open issue in the information fusion of Dempster-Shafer evidence theory (DST). We propose a new method, called the non-parametric plausibility ReliefF (NPReliefF) algorithm, to measure the reliability of information sources based on the criterion that sources providing more evidence aligned with “greater similarity within classes than between classes” are more reliable. The proposed method significantly improves the effect of normal BPAs and weakens the impact of abnormal BPAs on the fusion, even when abnormal BPAs are in the majority, thereby maximizing the correctness of the fusion results. Several numerical examples and an application of pattern recognition show the effectiveness and flexibility of the proposed methodology.
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