Decision Templates Based RBF Network for Tree-Structured Multiple Classifier Fusion
Multiclass pattern recognition problems (K > 2) can be decomposed by a tree-structured approach. It constructs an ensemble of K-1 individually trained binary classifiers whose predictions are combined to classify unseen instances. A key factor for an effective ensemble is how to combine its member outputs to give the final decision. Although there are various methods to build the tree structure and to solve the underlying binary problems, there is not much work to develop new combination methods that can best combine these intermediate results. We present here a trainable fusion method that integrates statistical information about the individual outputs (clustered decision templates) into a Radial Basis Function (RBF) network. We compare our model with the decision templates combiner and the existing nontrainable tree ensemble fusion methods: classical decision tree-like approach, product of the unique path and Dempster-Shafer evidence theory based method.
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