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首页> 外文期刊>Journal of Intelligent Information Systems >Bootstrapping rule induction to achieve rule stability and reduction
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Bootstrapping rule induction to achieve rule stability and reduction

机译：自举规则归纳以实现规则的稳定性和简化性

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摘要

Most rule learning systems posit hard decision boundaries for continuous attributes and point estimates of rule accuracy, with no measures of variance, which may seem arbitrary to a domain expert. These hard boundaries/points change with small perturbations to the training data due to algorithm instability. Moreover, rule induction typically produces a large number of rules that must be filtered and interpreted by an analyst. This paper describes a method of combining rules over multiple bootstrap replications of rule induction so as to reduce the total number of rules presented to an analyst, to measure and increase the stability of the rule induction process, and to provide a measure of variance to continuous attribute decision boundaries and accuracy point estimates. A measure of similarity between rules is also introduced as a basis of multidimensional scaling to visualize rule similarity. The method was applied to perioperative data and to the UCI (University of California, Irvine) thyroid dataset.
机译：大多数规则学习系统为规则属性的连续属性和点估计设置硬性决策边界，而没有方差的度量，这对于领域专家而言似乎是任意的。由于算法的不稳定性，这些硬边界/点随训练数据的微小扰动而变化。此外，规则归纳通常会产生大量必须由分析人员过滤和解释的规则。本文介绍了一种在规则引导的多个自举复制上组合规则的方法，以减少呈现给分析人员的规则总数，以衡量和提高规则诱导过程的稳定性，并提供对连续变化的度量。属性决策边界和准确性点估计。还引入了规则之间的相似性度量作为多维缩放的基础，以可视化规则相似性。该方法应用于围手术期数据和UCI（加利福尼亚大学尔湾分校）甲状腺数据集。

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