Data Dependence in Combining Classifiers

It has been accepted that multiple classifier systems provide a platform for not only performance improvement, but more efficient and robust pattern classification systems. A variety of combining methods have been proposed in the literature and some work has focused on comparing and categorizing these approaches. In this paper we present a new categorization of these combining schemes based on their dependence on the data patterns being classified. Combining methods can be totally independent from the data, or they can be implicitly or explicitly dependent on the data. It is argued that data dependent, and especially explicitly data dependent, approaches represent the highest potential for improved performance. On the basis of this categorization, an architecture for explicit data dependent combining methods is discussed. Experimental results to illustrate the comparative performance of some combining methods according to this categorization is included.