Fusion Methods for Equal and Unequal Classifier Systems

We investigate a number of parameters commonly affecting the design of a multiple classifier system in order to find when fusing is most beneficial. We extend our previous investigation to the case where unequal classifiers are combined. Results indicate that Sum is not affected by this parameter, however, Vote degrades when a weaker classifier is introduced in the combining system. This is more obvious when estimation error with uniform distribution exists

Keywords:

Fusion , bayes error , classifier combination , Vote , Sum