Research On High-Performance Feature Selection And Text Categorization

Absolutely, drwaing the valued information from the large quantity of miscellaneous text is a hard assignment, while text categorization is just that solution to implement this. Among which, the feture selection and test categorization arithmetic are the two key research directions. Regarding the feture selection, the goal of it is to select the most representative feature, by which the text space can be cut down. At the same time, not only the text categorization efficiency is enhanced, but also the categorized precision is improved by avoiding voice chatractors. On the other side, the latter on is a strong weapon to advance the categorization effect.Under the conditions that the existed feature selection method have not taken advantage of the useful term frequency information, and being short of qualitative analysis. This dissertation achieves as following, proposing a feature selection method based on the term frequency, analyzing qualitatively to feature selection method, innovating the constrained condition and steps of construting high efficiency feture selection method, formatting a high efficiency feature selection method and proving the above method by experiments.