Dynamic classifier selection based on multiple classifier behaviour

A dynamic classifier selection (DCS) method using multiple classifier systems(MCS) is proposed in a study. The proposed DCS method is based on the concepts of 'classifier's local accuracy' (CLA) and MCB. The basic idea is to estimate the accuracy of each classifier in a local region of the feature space surrounding an unknown test pattern, and then to select the classifier with the highest value of this local accuracy to classify the test pattern. the k-nearest neighbors of the test pattern are first identified in the training, or validation, data to define such a local region and compute CLAs.