Abstract :
Recent research efforts dedicated to classifier fusion have made it clear that combining performance strongly depends on careful selection of classifiers. Classifier performance depends, in turn, on careful selection of features, which on top of that could be applied to different subsets of the data. On the other hand, there is already a number of classifier fusion techniques available and the choice of the most suitable method relates back to the selection in the classifier, feature and data spaces. Despite this apparent selection multidimensionality, typical classification systems either ignore the selection altogether or perform selection along only single dimension, usually choosing the optimal subset of classifiers. The presented multidimensional selection sketches the general framework for the optimised selection carried out simultaneously on many dimensions of the classification model. The selection process is controlled by the specifically designed genetic algorithm, guided directly by the final recognition rate of the composite classifier. The prototype of the 3-dimensional fusion-classifier-feature selection model is developed and tested on some typical benchmark datasets.
