结合改进非负矩阵分解的模糊网页文本分类算法

通过构建向量空间模型可以获得表征网页数据的词-文本权重矩阵,然而直接基于此高维矩阵进行分类学习效率较低,为此提出一种结合改进非负矩阵分解的模糊网页文本分类算法.首先,通过迭代的归一化压缩非负矩阵分解将高维的原数据映射到低维语义空间,以降低问题的复杂性.然后,将模糊逻辑引入分类模型,通过特征词与类别的模糊隶属度来生成文本的类别模糊集,以解决确定性矩阵难以判定语义模糊词所属类别的问题.实验结果表明,与其他方法相比,所提出的分类算法具有较高的分类准确度和较好的时间性能.

Fuzzy webpage text classification algorithm combined with improved NMF

An item-document weight matrix representing the web pages could be generated by constructing the vector space model. Since the efficiency of direct classification through the high-dimensional matrix is relatively low, a fuzzy webpage text classification algorithm combined with improved nonnegative matrix factorization (NMF) is presented. Firstly, the original high-dimensional data are mapped into the low-dimensional semantic space via an iterative normalized compression NMF(NCMF) to reduce the complexity of the problem. Secondly, in order to solve the problem of categorizing ambiguous words by using deterministic matrices, fuzzy logic is incorporated into the classification model, where the fuzzy categorization set of the document is constructed with the fuzzy membership degree between features and categories. Comparative experiment results demonstrate the proposed classification algorithm possesses higher accuracy and better time performance.