A method of combining multiple classifiers based on evidence theory
Yan Wang · Dianji yu kongzhi xuebao · 2001
The paper proposes a method of combining multiple classifiers based on evidence reasoning. First, the uncertainty of each classifier's output in modeled. Then, all the classification results are integrated by the use of D-S combination rule. Furthermore, a fast algorithm is derived to deal with the complex computation in evidence combination. In this method, we use multiple classifiers with moderate performance and low-dimension instead of a classifier with high-dimension input space and/or high performance. Thus, it gives a good resolution of designing a classifier with high recongnition rate for high-dimension input prob- lem. Finally, the method is applied to handwritten digit recognition system where the results demnostrate its effectiveness.