Classifier geometrical characteristic comparison and its application in classifier selection
Classifier selection is an important step in designing multiple classifier systems. In this paper classifier geometrical characteristic comparison methods including volume occupation difference comparison and decision boundary curvature difference comparison are proposed. Based on the comparison which is directly carried out on training samples, difference between classifiers can be measured. The usefulness of these methods to measure the difference between classifiers decision space and their influence on recognition rate of multiple classifier systems are confirmed by a series of experiments. Then a classifier selection strategy based on these comparison methods is introduced. The experiment results show that it is a useful selection strategy for selecting classifiers to combine.
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