This work borrows the traditional Pittsburgh-style representation from Genetic-Based Machine Learning and evaluates its performance in artificial immune systems (AIS) for classification. Our main goal is to select as few instances as possible to represent the data from the training set without losing accuracy. The new representation is tested in a modified version of a clonal selection algorithm, where the antibodies represent lists of prototypes instead of a single one. The generated method, named Clonal Selection Prototypes Generator, was tested in 10 UCI datasets and compared to other seven methods that execute the same task. Results showed that the proposed method is very good at considering a trade-off between the number of prototypes generated and the accuracy of the system.