A new classification method using the generalized basic probability assignment
Yongchuan Tang, Lei Wu, Yubo Huang, Deyun Zhou · 2023
Classification with incomplete information processing under uncertain circumstance is still an open issue. In this study, the Dempster-Shafer evidence theory is extended to the generalized evidence theory in which this problem is addressed from the perspective of open world assumption. An improved method is proposed to model the incomplete information where the generalized basic probability assignment (GBPA) is generated by using the Gaussian distribution model. First, we constructed the Gaussian distribution based on the mean and variance calculated from the training set. Then, we modeled the potential incomplete information with the GBPA of empty set by matching the test sample with the constructed Gaussian distribution model. Third, we identified and recognized the unknown object by fusing the data with the generalized combination rule. Experiment in classification as well as a comparative study is illustrated to show the superiority and efficiency of this method.