A Sensitivity Clustering Method for Memetic Training of Radial Basis Function Neural Networks

In this paper, we propose a memetic algorithm (MA) for classifier optimization based on a clustering method that applies the k-means algorithm over a specific derived space. In this space, each classifier or individual is represented by the set of the accuracies of the classifier for each class of the problem. The proposed sensitivity clustering is able to obtain groups of individuals that perform similarly for the different classes. Then, a representative of each group is selected and it is improved by a local search procedure. This method is applied in specific stages of the evolutionary process. The sensitivity clustering process is compared to a clustering process applied over the Tridimensional space that represent the behaviour of the classifier over each training pattern, where n is the number of patterns. This second method clearly results in a higher computational cost. The comparison is performed in ten imbalanced datasets, including the minimum sensitivity results (i.e. the accuracy for the worst classified class). The results indicate that, although in general the differences are not significant, the sensitivity clustering obtains the best performance for almost all datasets both in accuracy and minimum sensitivity, involving a lower computational demand.