GDL

Feature Selection Method Based on Improved Document Frequency

Feature selection is an important part of the process of text classification, there is a direct impact on the quality of feature selection because of the evaluation function. Document frequency (DF) is one of several commonly methods used feature selection, its shortcomings is the lack of theoretical basis on function construction, it will tend to select high-frequency words in selecting. To solve the problem, we put forward a improved algorithm named DFM combined with class distribution of characteristics and realize the algorithm with programming, DFM were compared with some feature selection method commonly used with experimental using support vector machine, as text classification .The results show that, when feature selection, the DFM methods performance is stable at work and is better than other methods in classification results.

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