Evolutionary automated classification

The problem of classification can be stated as: “Given a set of a attributes and the characterization of n objects by means of a attribute values for each object, find an optimal classification (partition) of the n objects into classes”. The first step in supervised classification is to find k, the number of classes into which the objects have to be split in order to obtain the optimum classification. The dynamic kernels algorithm searches for a classification which maximizes B [=Σ_j=1^kn _jd(G_j,G), where d(G_j,G) denotes the distance between the points G_j and G, G is the center of gravity of the n given points and G_j are the centers of gravity of the k classes]. Unfortunately, the maximum value of B is not the same for different values of k, so the Huygens theorem can only be used for comparing classifications of the given objects into a fixed number of classes. We propose an evolution program for this problem. It simultaneously searches for both the optimum number of classes and the optimal classification. So far, we have implemented our approach for the case of two attributes with continuous values. Straightforward adaptations are under way for allowing the user to input any reasonable number of (continuous or discrete) attributes