A learning model for multiple-prototype classification of strings

Abstract

An iterative learning method to update labeled string prototypes for a 1-nearest prototype (1-np) classification is introduced. Given a (typically reduced) set of initial string prototypes and a training set, it iteratively updates prototypes to better discriminate training samples. The update rule, which is based on the edit distance, adjusts a prototype by removing those local differences which are both frequent with respect to same-class closer training strings and infrequent with respect to different-class closer training strings. Closer training strings are defined by unsupervised clustering. The process continues until prototypes converge. Its main innovation is to provide a non-random local update rule to "move" a string prototype towards a number of string samples. A series of learning/classification experiments show a better 1-np performance of the updated prototypes with respect to the initial ones, that were originally selected to guarantee a good classification.