新的结合瓦信息和粗糙集的特征选择

摘要

特征选择是文本分类的一个重要步骤.分析了互信息,针对其不足引进了粗糙集给出了一个基于关系积的属性约简算法,并以此为基础提出了一个新的适用于海量文本数据集的特征选择方法.该方法使互信息进行特征初选,利用基于关系积的属性约简算法消除冗余词.实验结果表明此种特征选择方法的微平均F和宏平均F较高.%Feature selection is an important step in text categorization. MI is analyzed,according to deficiency of MI,RS is introduced and an attribute reduction algorithm based on attribute union is proposed. A new feature selection method combined MI with the proposed attribute reduction algorithm is presented which is suitable for massive text data sets. The method uses MI to select features,and employs the proposed attribute reduction algorithm to eliminate redundancy. The experimental results show that micro average F1 and macro average F1 of the new method are higher.