Discovery of Exceptions: A Step towards Perfection

Abstract

It is interesting to discover exceptions, as they dispute the existing knowledge and have elements of unexpectedness and surprise. As exceptions focus on a very small portion of data, discovering exceptions still remains a great challenge. A censored production rule (CPR) is a special kind of knowledge structure that augments exceptions to their corresponding commonsense rules of high generality and support. This paper proposes discovery of decision rules in the form of censored production rules by employing a genetic algorithm approach. Results confirm that the proposed discovery of decision rules in the form of CPRs is comprehensible and interesting. Using CPRs as underlying knowledge structure for rule mining provides an excellent mechanism for exception handling and approximate reasoning. Moreover, discovering exceptions through CPRs enhances the predictive accuracy of the classifier.