论文标题
评估基于规则的分类系统的质量
On Evaluating the Quality of Rule-Based Classification Systems
论文作者
论文摘要
两个指标通常用于评估基于规则的分类系统的质量：预测准确性，即系统成功复制学习数据和覆盖范围的能力，即适用系统构成逻辑规则的可能情况的比例。在这项工作中，我们声称这两个指标可能不足，并且可能需要制定其他质量措施。从理论上讲，我们表明，呈现“良好”预测准确性和覆盖范围的分类系统可以通过示例来微不足道地改进，并用示例来说明这一主张。
Two indicators are classically used to evaluate the quality of rule-based classification systems: predictive accuracy, i.e. the system's ability to successfully reproduce learning data and coverage, i.e. the proportion of possible cases for which the logical rules constituting the system apply. In this work, we claim that these two indicators may be insufficient, and additional measures of quality may need to be developed. We theoretically show that classification systems presenting "good" predictive accuracy and coverage can, nonetheless, be trivially improved and illustrate this proposition with examples.
