关键词: 文本分类, 维度消减, 差异&mdash, 相似矩阵

Abstract: Due to the huge amount of text documents and their vocabulary, document spaces are commonly of high dimensionality, and many automatical text categorization algorithms can not get their best performences directly. Difference-similitude Matrix-based (DSM) method reduces dimensionality to a great extend. Pre-classified collection is represented as a item-document matrix after preprocessing, then transmitted into a DSM, in which documents in the same classes are depicted with similitude while documents in different classes with difference. The method generates an item set of low dimensionality and a set of classification rules after dealing with the DSM. Results of experiments suggest that DSM-based method could achieve high attribute reduction degree and sample reduction degree with good classification quality.

Key words: text categorization, dimensionality reduction, DSM(Difference-Similitude Matrix)