Multi-View Forests of Tree-Structured Radial Basis Function Networks Based on Dempster- Shafer Evidence Theory
Mohamed Farouk Abdel Hady, Günther Palm, Friedhelm Schwenker · The European Symposium on Artificial Neural Networks · 2008
An essential requirement to create an accurate classifier ensemble is the diversity among the individual base classifiers. In this paper, Multi-View Forests, a method to construct ensembles of tree-structured radial basis function (RBF) networks using multi-view learning is proposed. In Multi-view learning it is assumed that the patterns to be classified are described by multiple feature sets (views). Multi-view Forests have been evaluated by using a benchmark data set of handwritten digits recognition. Results show that multi-view learning can improve the performance of the ensemble by enforcing the diversity among the individual classifiers.