In order to solve the difficulty of obtaining the basic probability assignment(BPA)and the low applicability of genera-tion model in D-S evidence theory application,a basic probabilistic assignment generation method combining nearest neighbor density and information correction was proposed.A single focal element BPA function was generated based on the distance be-tween the peak density point and the sample data derived from the K-nearest neighbor(KNN)algorithm;the values were as-signed to the whole subset of events by belief x2 divergence and BPA information was modified based on confidence.The im-proved belief entropy formula was utilized to compute the uncertainty weight of each piece of evidence for evidence redistribu-tion.The generated BPA is utilized to solve the practical application problems of few samples and imbalanced class samples,and the diagnostic accuracies of the proposed method are all over 85％,which are better than other methods,as verified by multiple datasets.
维普
万方数据
