RRULES is presented as an improvement and optimization over RULES, a simple inductive learning algorithm for extracting IF-THEN rules from a set of training examples. RRULES optimizes the algorithm by implementing a more effective mechanism to detect irrelevant rules, at the same time that checks the stopping conditions more often. This results in a more compact rule set containing more general rules which prevent overfitting the training set and obtain a higher test accuracy. Moreover, the results show that RRULES outperforms the original algorithm by reducing the coverage rate up to a factor of 7 while running twice or three times faster consistently over several datasets.

翻译：RRULES被描述为对RULES的一种改进和优化,RULES是一种简单的感应式学习算法,用来从一组培训实例中提取 IF-HEN 规则。RRULES 优化了算法,方法是实施一个更有效的机制来检测不相关的规则,同时更经常地检查停工条件。这导致形成一套更为紧凑的规则,其中载有更一般性的规则,防止过配训练,并获得更高的测试准确性。此外,结果显示RRRULES通过将覆盖率降低到7倍,同时比几个数据集连续运行两倍或三倍的速度,将覆盖率降低到7倍,从而优于原始算法。