A fuzzy fusion algorithm to combine multiple classifiers
Bor‐Chen Kuo, Chih-Sheng Huang, Hsiang-Chuan Liu, Chih‐Cheng Hung · 2009
Combining multiple classifiers is a natural way to discover useful information and improve the performances of individual classifiers. It's based on the combination of the outputs of an ensemble of different classifiers. When interactions exist in combining multiple classifiers, fuzzy integral would be a valid method to fuse multiple classifiers. In this fuzzy fusion approach, the fuzzy measure plays an important role. Liu proposed a novel fuzzy measure, L-measure, which is more sensitive than some common measures, like ¿-measure, P-measure and V-measure. In this paper, we would combine the multiple classifiers by Choquet integral wtih this Z-measure.