From the introduction: A DCS (Dynamic Classifier Selection) method using MCB (Multiple Classifier Behaviour) is proposed. The DCS method we propose, is based on the concepts of “Classifier’s Local Accuracy” (CLA) and MCB. In particular, we exploit MCB information to compute CLA. 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. In order to define such a local region and compute CLAs, the \(k\)-nearest neighbours of the test pattern are first identified in the training, or validation, data. The \(k\)-nearest neighbours characterized by MCBs “similar” to the one of the unknown test patterns are then selected to compute CLAs and perform CDS. This method is described in detail in the next Section. Experimental results and comparisons are reported in Section 3.