Prescreening of Candidate Rules Using Association Rule Mining and Pareto-optimality in Genetic Rule Selection
Prescreening of Candidate Rules Using Association Rule Mining and Pareto-optimality in Genetic Rule Selection
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DOI：
10.1007/978-3-540-74827-4_64
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
H. Ishibuchi;I. Kuwajima;Y. Nojima
中科院分区：
文献类型：
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作者：
H. Ishibuchi;I. Kuwajima;Y. Nojima
遗传规则选择是一种设计高精度、高可解释性分类器的方法。它从大量的候选规则中搜索少量的简单分类规则。遗传规则选择的有效性很大程度上取决于候选规则的选择。当我们有成千上万的候选规则时，很难有效地搜索出它们的好子集。另一方面，如果我们只有几个候选规则，规则选择就没有意义。在本文中，我们研究了关于支持度和置信度的帕累托最优和近似帕累托最优规则作为候选规则在遗传规则选择中的应用。
Genetic rule selection is an approach to the design of classifiers with high accuracy and high interpretability. It searches for a small number of simple classification rules from a large number of candidate rules. The effectiveness of genetic rule selection strongly depends on the choice of candidate rules. If we have hundreds of thousands of candidate rules, it is very difficult to efficiently search for their good subsets. On the other hand, if we have only a few candidate rules, rule selection does not make sense. In this paper, we examine the use of Pareto-optimal and near Pareto-optimal rules with respect to support and confidence as candidate rules in genetic rule selection.