Knowledge discovery-based multiple classifier fusion: a generalized rough set method

A novel knowledge discovery method to multiple classifier fusion is proposed. In the new method, all base classifiers are viewed as predictors relating to domain knowledge, and they may be allowed to operate in different feature spaces. Then the beliefs assigned to each base classifier are generated automatically from the established decision tables (DTs). For this purpose, two types of belief structures on DT are investigated based on generalized rough set model and Dempster-Shafer theory (DST). Correspondingly, two fusion approaches are designed based on the belief structures and the heuristic fusion function. Compared with plurality voting, the vegetation classification experiment on hyperspectral remote sensing images shows that the performance of the classification can be improved further by using the proposed method