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中文摘要：
基于规则学习的文本分类算法RIPPER具有易理解、易优化、高效率等特点，但是当规则所涉及的特征项很多的时候，上述优点不复存在。基于层次的规则学习算法hRIPPER采用了层次架构对RIPPER进行了改进，但其对特征项的过滤仍然有限。针对RIPPER，hRIPPER在规则学习过程中出现的问题，对规则学习的分类算法进行改进，提出了一种改进的基于规则学习的文本分类算法iRIPPER，在规则学习的同时进一步过滤噪音特征项。实验证明，该方法不但有效地提取了特征项，生成较少的规则，提高了算法的准确率和召回率，而且缩短了生成规则的时间，从而改进了规则学习分类算法的性能。
英文摘要：
The ntle-based text categorization algorithm RIPPER was specialized with easy understanding, quick optimization, and high efficiency. However, when the ntle refers to too many features, not only were the above advantages apparently weakened, but also the performance of the algorithm decreases. The hierarchy-based hRIPPER though uses hierarchical feature selection and can still not filter features fully. Then an improved text categorization algorithm iRIPPER was proposed to solve the problems in the learning process of RIPPER and hRIPPER, which filters features more thoroughly during the learning process. The experiment proves that it selects features effectively, generates fewer rules, and reduces the time in the growing process. Therefore it improves the performance of the ntle-based text categorization.
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