使用频繁项集挖掘进行组合布尔模型的嫁接
Grafting for Combinatorial Boolean Model using Frequent Itemset Mining
AI总结：
针对组合布尔模型（CBM）学习计算成本过高的问题，提出GRAB算法，通过将L1正则化损失最小化转化为加权频繁项集挖掘，在保证可解释性的同时实现了计算效率、预测准确性和知识发现的有效提升。
摘要：
本文介绍了组合布尔模型（CBM），它被定义为布尔属性合取的线性组合的类。本文探讨了从带标签数据中学习CBM的问题。CBM具有高度的知识可解释性，但对其的朴素学习需要相对于数据维度和样本量呈指数级增长的计算时间。为了克服这一计算困难，我们提出了一种算法GRAB（GRAfting for Boolean datasets），它能在$L_1$正则化损失最小化框架内高效学习CBM。GRAB的核心思想是将损失最小化问题转化为加权频繁项集挖掘，其中频繁模式是可高效计算的。我们使用基准数据集实证证明GRAB在计算效率、预测准确性和知识发现方面是有效的。
英文摘要：
This paper introduces the combinatorial Boolean model (CBM), which is defined as the class of linear combinations of conjunctions of Boolean attributes. This paper addresses the issue of learning CBM from labeled data. CBM is of high knowledge interpretability but naïve learning of it requires exponentially large computation time with respect to data dimension and sample size. To overcome this computational difficulty, we propose an algorithm GRAB (GRAfting for Boolean datasets), which efficiently learns CBM within the $L_1$-regularized loss minimization framework. The key idea of GRAB is to reduce the loss minimization problem to the weighted frequent itemset mining, in which frequent patterns are efficiently computable. We employ benchmark datasets to empirically demonstrate that GRAB is effective in terms of computational efficiency, prediction accuracy and knowledge discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
