用关联规则提升分类模型的预测与解释能力
Leveraging Association Rules for Better Predictions and Better Explanations
- 从数据中挖掘带否定的关联规则，增强树模型性能
- 相比基线，预测准确率提升且解释更简洁
- 适合需要可解释性的分类任务，如医疗决策
我们提出一种结合数据与知识的新型分类方法。通过数据挖掘从数据中提取关联规则（可能包含否定），并将其用于提升基于树的模型（决策树和随机森林）在分类任务中的预测性能。同时，利用这些规则生成更具泛化能力的反向推理解释，相比不考虑规则的情况，解释规模更小。实验表明，对于所考察的两类树模型，该方法在预测性能和解释简洁性上均有提升。
原文摘要 · Abstract (English)
We present a new approach to classification that combines data and knowledge. In this approach, data mining is used to derive association rules (possibly with negations) from data. Those rules are leveraged to increase the predictive performance of tree-based models (decision trees and random forests) used for a classification task. They are also used to improve the corresponding explanation task through the generation of abductive explanations that are more general than those derivable without taking such rules into account. Experiments show that for the two tree-based models under consideration, benefits can be offered by the approach in terms of predictive performance and in terms of explanation sizes.
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