Local ordinal classification

Abstract

Given ordered classes, one is not only concerned to maximize the classification accuracy, but also to minimize the distances between the actual and the predicted classes. This paper offers an organized study on the various methodologies that have tried to handle this problem and presents an experimental study of these methodologies with the proposed local ordinal technique, which locally converts the original ordinal class problem into a set of binary class problems that encode the ordering of the original classes. The paper concludes that the proposed technique can be a more robust solution to the problem because it minimizes the distances between the actual and the predicted classes as well as improves the classification accuracy. © 2006 International Federation for Information Processing.