Adaptively Evidential Weighted Classifier Combination
Liguo Fei, Bingyi Kang, Van‐Nam Huynh, Yong Sheng Deng · viXra · 2017
Classifier combination plays an important role in classification. Due to the efficiency to handle and fuse uncertain information, Dempster-Shafer evidence theory is widely used in multi-classifiers fusion. In this paper, a method of adaptively evidential weighted classifier combination is presented. In our proposed method, the output of each classifier is modelled by basic probability assignment (BPA). Then, the weights are determined adaptively for individual classifier according to the uncertainty degree of the corresponding BPA. The uncertainty degree is measured by a belief entropy, named as Deng entropy. Discounting-and-combination scheme in D-S theory is used to calculate the weighted BPAs and combine them for the final BPA for classification. The effectiveness of the proposed weighted combination method is illustrated by numerical experimental results.