Abstract
A nearest-neighbor classifier compares an unclassified object to a set of pre-classified examples and assigns to it the class of the most similar of them (the object's nearest neighbor). In some applications, many pre-classified examples are available and comparing the object to each of them is expensive. This motivates studies of methods to remove redundant and noisy examples. Another strand of research seeks to remove irrelevant attributes that compromise classification accuracy. The paper suggests to use the genetic algorithm to address both issues simultaneously. Experiments indicate considerable reduction of the set of examples, and of the set of attributes, without impaired classification accuracy. The algorithm compares favorably with earlier solutions.