A Feature Selection Approach Based on Rough Set Theory
Yanli Ma · Journal of Hebei North University · 2009
Objective Characteristic's quality can affect precision of the text classification,therefore,it is important to choose one good feature selection method in the text classification.Methods Rough Set Theory is an effective tool used to analyze imprecise data and discover the latent rules in these data.In this paper a feature selection method based on rough set is presented.Results The experiment result indicated that this method could reduce the characteristic dimension by using reduction theory of rough set and guarantee th performance of text categorization.The using of method could get a better classified effect compared with the present method when getting the feature selection.Conclusion Rough set's attribute reduction theory may be used in the rule extraction and the feature selection.Using the rough set's attribute reduction theory,the ideal classified effect can be obtained when conducting a feature selection.