文章基本信息

标题：Worst-Case Analysis of Rule Discovery Based on Generality and Accuracy
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作者：Einoshin Suzuki
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2002
卷号：17
期号：5
页码：630-637
DOI：10.1527/tjsai.17.630
出版社：The Japanese Society for Artificial Intelligence
摘要：In this paper, we perform a worst-case analysis of rule discovery based on generality and accuracy. A rule is defined as a probabilistic constraint of true assignment to the class attribute for corresponding examples. In data mining, a rule can be considered as representing an important class of discovered patterns. We accomplish the aforementioned objective by extending a preliminary version of PAC learning, which represents a worst-case analysis for classification. Our analysis consists of two cases: the case in which we try to avoid finding a bad rule, and the case in which we try to avoid overlooking a good rule. Discussions on related work are also provided for PAC learning, multiple comparison, analysis of association rule discovery, and simultaneous reliability evaluation of a discovered rule.
关键词：rule discovery ; worst-case analysis ; PAGA discovery ; Chernoff bounds