结合差别对象对集的综合性特征选择方法

Feature selection method combined discernibility object pair set

特征选择是文本分类的关键步骤之一,所选特征子集的优劣直接影响文本分类的结果。分析了词频法和文档频法并总结了其缺陷,给出了一个改进的文档频方法;引进粗糙集理论,提出了一个属性约简算法;最后提出了一个新的特征选择方法。该特征选择方法使用改进的文档频初选特征并用所提属性约简算法消除冗余。仿真结果表明该特征选择方法性能较好。

Feature selection is one of the key steps in text categorization.The selected feature subset directly influences results of text categorization.Firstly,word frequency and document frequency are analyzed,and their deficiencies are summarized.Then an improved document frequency is presented.Next,it introduced RS and provided a new attribute reduction algorithm based on discernibility object pair set.Finally,a new feature selection method is proposed.The new feature selection method uses the improved document frequency to select feature and employs the proposed attribute reduction algorithm to eliminate redundancy.The simulation results show that the new feature selection method is promising.