Choosing the initial set of exemplars when learning with an NGE-based system

In the original proposal of the NGE (nested generalized exemplar) system, the induction of a concept is based on an initial set of training examples (named seeds) that are randomly chosen. The number of examples in this set is arbitrary, generally determined by the user of the system. It can be seen empirically, that the final results are influenced by the initial choice of the seeds. We propose and investigate other alternative methods for choosing seeds and empirically evaluate their impact on the learning results in seven knowledge domains, as far as accuracy and number of expressions describing the concepts are concerned. In spite of the additional time investment associated with using a clustering method and, assuming that accuracy of the induced concept is of major importance, experiments have shown that choosing the initial set of seeds as the center of clusters can be the best option.