Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. Instead, we propose an efficient algorithm that aims at finding the best rule covering each training example in a greedy optimization consisting of one specialization and one generalization loop. These locally optimal rules are collected and then filtered for a final rule set, which is much larger than the sets learned by conventional rule learning algorithms. A new example is classified by selecting the best among the rules that cover this example. In our experiments on small to very large datasets, the approach’s average classification accuracy is higher than that of state-of-the-art rule learning algorithms. Moreover, the algorithm is highly efficient and can inherently be processed in parallel without affecting the learned rule set and so the classification accuracy. We thus believe that it closes an important gap for large-scale classification rule induction.

传统的规则学习算法旨在找到一组简单的规则，其中每个规则涵盖尽可能多的示例。在本文中，我们认为以这种方式找到的规则可能不是对它们涵盖的每个示例的最佳解释。相反，我们提出了一种有效的算法，旨在在由一个专门化和一个泛化循环组成的贪婪优化中找到覆盖每个训练示例的最佳规则。收集这些局部最优规则，然后对其进行过滤以获得最终规则集，该规则集比传统规则学习算法学习的集大得多。通过在涵盖该示例的规则中选择最佳规则来对新示例进行分类。在我们对小到非常大的数据集的实验中，该方法的平均分类精度高于最先进的规则学习算法。此外，该算法非常高效，并且可以在不影响学习规则集和分类精度的情况下进行并行处理。因此，我们认为它缩小了大规模分类规则归纳的重要差距。