A parallel genetic algorithm approach for automated discovery of censored production rules
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This article presents an algorithm that seeks every possible exception rule that violates a commonsense rule and satisfies several assumptions of simplicity. Exception rules, which represent systematic deviation from commonsense rules, are often found interesting. Discovery of pairs that consist of a commonsense rule and an exception rule, resulting from undirected search for unexpected exception rules, was successful in various domains. In the past, however, an exception rule represented a change of conclusion caused by adding an extra condition to the premise of a commonsense rule. That approach formalized only one type of exception and failed to represent other types. To provide a systematic treatment of exceptions, we categorize exception rules into 11 categories, and we propose a unified algorithm for discovering all of them. Preliminary results on 15 real-world datasets provide an empirical proof of effectiveness of our algorithm in discovering interesting knowledge. The empirical results also match our theoretical analysis of exceptions, showing that the 11 types can be partitioned in three classes according to the frequency with which they occur in data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 673–691, 2005.