Design of Multiple Classifier Systems
نویسندگان
چکیده
In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this chapter, the problem of multiple classifier system design is discussed and the reader is provided with a critical survey of the state of the art. A formulation of the design problem that provides motivations for the different design methods described in the literature is proposed. In particular, such a formulation points out the rationale behind the so-called overproduce and choose design paradigm. Six design methods based on this paradigm are described and compared by experiments with three different data sets. Though these design methods have some interesting features, they do not guarantee an optimal multiple classifier system design for the classification task at hand. Accordingly, the main conclusion in this chapter is that optimal design is still an open problem.
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