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中文摘要：
网络信息的多样性和多变性给信息的管理和过滤带来极大困难,为加快网络信息的分类速度和分类精度,提出了一种基于模糊粗糙集的Web文本分类方法。采用机器学习的方法：在训练阶段,首先对Web文本信息预处理,用向量空间模型表示文本,生成初始特征属性空间,并进行权值计算;然后用模糊粗糙集算法来进行信息过滤,用基于模糊粗糙集的属性约简算法生成分类规则;最后利用知识库进行文档分类。在测试阶段,对未经预处理的文本直接进行关键属性匹配,经模糊粗糙因子加权后,用空间距离法分类。通过试验比较,该方法具有较好的分类效果。
英文摘要：
The diversity and variability of network information brings great difficulty to information management and information filtering.Put forward a method to Web document classification based on fuzzy-rough set in order to improve the speed and accuracy of network information classification and use machine learning method for training and testing Web document.In the training process,firstly,representing preprocessed Web documents by vector space model,forming initial attribution features space and conducting weight value computing.Then,conducting information filtering and reducing attribution feature space by fuzzy-rough set algorithm,forming classification rules.Finally,classifying documents by multiple knowledge bases.In the testing process,matching key attributes directly and computing weight value by the fuzzy-rough factor,then classifying document by space distance method.The experiment results and the comparison with others show that this Web document classification has good classification performance.
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