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Trained SVMs based rules extraction method for text classification

机译:基于训练SVM的文本分类规则提取方法

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The automatic text classification method aims to assign text files to one or more predefined categories according to the text information contained by all kinds of text format files.SVM is recognized as one of the most effective text classification methods for its high accuracy, but its black-box feature causes that the description of each category can not be given and explained. In this paper, a new rule extraction method for text classification based on trained SVMs is proposed to solve the bottleneck of SVMs. The Experiments show that the proposed approach can improve the validity of the extracted rules remarkably compared to C4.5 either in speed or accuracy.
机译:自动文本分类方法旨在根据各种文本格式文件所含的文本信息将文本文件分配给一个或多个预定义类别.SVM被识别为其高精度的最有效的文本分类方法之一,但它是黑色的 - 箱功能导致无法给出和解释每个类别的描述。本文提出了一种基于训练SVMS的文本分类的新规则提取方法,解决了SVM的瓶颈。实验表明,该方法可以在速度或准确性中提高提取的规则的有效性。

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