A framework for using multiple classifiers in a multiple-agent architecture
L.G. Vuurpijl, Lambert Schomaker · 1998
This paper describes a new framework using intelligent agents for pattern recognition. A justification for using alternatives to current classifier systems is given. The use of the framework, called iart, is tested on a digit recognition system. 1 Introduction The use of multiple classifiers and the combination of their classification results has gained considerable interest in the last few years. This approach has a powerful potential because it may exploit the advantages of different feature representations and classification methods. In general two architectures can be distinguished, 1) individual classifiers with some combination scheme and 2) multi-stage or hierarchical classifiers. Several combination schemes are possible, like majority vote, max/min/median rule, BKS [8], the Dempster-Shafer rule or Borda count. These use either class labels, rank order or score combinations. Classifiers use one or more components in a "pattern recognition pipeline". Pattern recognition modules ...