Prototype Selection Methods

in pattern recognition, supervised classifiers assign a class to unseen objects or prototypes. for classifying new prototypes a training set is used which provides information to the classifiers during the training stage. in practice, not all information in a training set is useful therefore it is possible to discard some irrelevant prototypes. this process is known as prototype selection and it is the main topic of this thesis. through prototype selection the training set size is reduced which allows reducing the runtimes in the classification and/or training stages of classifiers. several methods have been proposed for selecting prototypes however their performance is strongly related to the use of a specific classifier and most of the methods spend long time for selecting prototypes when large datasets are processed. in this thesis, four methods for selecting prototypes, which solve drawbacks of some methods in the state of the art are proposed. the first two methods are based on the sequential floating search and the two remaining methods are based on clustering and prototype relevance respectively.