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Classification rule mining using ant programming guided by grammar with multiple Pareto fronts

机译：使用具有多个Pareto前沿语法的蚂蚁编程进行分类规则挖掘

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This paper proposes a multi-objective ant programming algorithm for mining classification rules, MOGBAP, which focuses on optimizing sensitivity, specificity, and comprehensibility. It defines a context-free grammar that restricts the search space and ensures the creation of valid individuals, and its heuristic function presents two complementary components. Moreover, the algorithm addresses the classification problem from a new multi-objective perspective specifically suited for this task, which finds an independent Pareto front of individuals per class, so that it avoids the overlapping problem that appears when measuring the fitness of individuals from different classes. A comparative analysis of MOGBAP using two and three objectives is performed, and then its performance is experimentally evaluated throughout 15 varied benchmark data sets and compared to those obtained using another eight relevant rule extraction algorithms. The results prove that MOGBAP outperforms the other algorithms in predictive accuracy, also achieving a good trade-off between accuracy and comprehensibility.
机译：本文提出了一种用于挖掘分类规则的多目标蚂蚁编程算法MOGBAP，该算法着重于优化敏感性，特异性和可理解性。它定义了上下文无关的语法，该语法限制了搜索空间并确保了有效个人的创建，其启发式功能提供了两个互补的组成部分。此外，该算法从新的多目标角度解决了分类问题，该角度特别适合于此任务，该算法在每个类别中找到一个独立的个人Pareto前沿，从而避免了在测量不同类别的个人的适应度时出现的重叠问题。使用两个和三个目标对MOGBAP进行了比较分析，然后通过15个不同的基准数据集对它的性能进行了实验评估，并与使用其他八种相关规则提取算法获得的性能进行了比较。结果证明，MOGBAP在预测准确度方面优于其他算法，并且在准确度和可理解性之间取得了良好的折衷。

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