Learning and classification of monotonic ordinal concepts

Abstract

Ordinal reasoning plays a major role in human cognition. This paper identifies an important class of classification problems of patterns taken from ordinal domains and presents efficient, incremental algorithms for learning the classification rules from examples. We show that by adopting a monotonicity assumption of the output with respect to the input, inconsistencies among examples can be easily detected and the number of possible classification rules substantially reduced. By adopting a conservative classification criterion, the required number of rules further decreases. The monotonicity and conservatism of the classification also enable the resolution of conflicts among inconsistent examples and the graceful handling of don't knows and don't cares during the learning and classification phases. Two typical examples in which the suggested classification model works well are given. The first example is taken from the financial domain and the second from machining. Copyright © 1989, Wiley Blackwell. All rights reserved