Whitebox Induction of Default Rules Using High-Utility Itemset Mining
Whitebox Induction of Default Rules Using High-Utility Itemset Mining
复制标题
使用高效用项集挖掘白盒归纳默认规则
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
G. Gupta
中科院分区：
文献类型：
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作者：
Farhad Shakerin;G. Gupta
我们提出了一个快速和可扩展的算法，从统计学习模型中归纳出非单调逻辑程序。我们减少了搜索最佳条款的高效用项目集挖掘（HUIM）问题的实例的问题。在HUIM问题中，特征值和它们的重要性分别被视为事务和效用。我们利用TreeExplainer，可解释的AI工具SHAP的快速和可扩展的实现，从集成树模型中提取局部重要特征及其权重。我们的实验与UCI标准基准表明显着改善的分类评估指标和训练时间相比，ALEPH，一个国家的最先进的归纳逻辑编程（ILP）系统。
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of the High-Utility Itemset Mining (HUIM) problem. In the HUIM problem, feature values and their importance are treated as transactions and utilities respectively. We make use of TreeExplainer, a fast and scalable implementation of the Explainable AI tool SHAP, to extract locally important features and their weights from ensemble tree models. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics and training time compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.