论文标题
一种用于解释具有决策规则的分类数据集的嵌套遗传算法
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules
论文作者
论文摘要
本文我们的目标是自动提取一组决策规则（规则集），该规则可以最好地解释分类数据集。首先，从对数据集训练的一组决策树中提取了一大批决策规则。规则集应简洁，准确，具有最大覆盖范围和最小数量的不一致。该问题可以正式化为已知是NP hard的加权预算最大覆盖问题的修改版本。为了有效地解决组合优化问题，我们引入了一种嵌套的遗传算法，然后将其用于为十个公共数据集提供解释。
Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set. First, a large set of decision rules is extracted from a set of decision trees trained on the data set. The rule set should be concise, accurate, have a maximum coverage and minimum number of inconsistencies. This problem can be formalized as a modified version of the weighted budgeted maximum coverage problem, known to be NP-hard. To solve the combinatorial optimization problem efficiently, we introduce a nested genetic algorithm which we then use to derive explanations for ten public data sets.
