摘要
提出了一种面向机械领域的文本分类器.特征选择采用基于文档频率的特征提取法和灰色关联度计算相结合的方法,有效降低了特征维数,削弱了特征词之间的关联,为采用贝叶斯分类创造了条件.分类阶段引进了基于类别区分度的加权因子对朴素贝叶斯分类器进行优化.实验证明,该分类器能够有效地提高机械领域文本分类的召回率和正确率,具有较好的使用效果.
A information text classifier of machinery-oriented is proposed in this paper.The method of document frequency and grey relation analysis are used to select feature,which reduce the characteristics of dimensionality,weaken relation between feature words and create the conditions for the Bayes.The Bayesian Classifier is ameliorated by using word＇s kinds-difference as weighted factor.The experimental results indicate that the classifier is able to improve recall and precision,and is useful in practice.
出处
《微电子学与计算机》
CSCD
北大核心
2012年第4期142-145,共4页
Microelectronics & Computer
基金
陕西省自然科学基金(2009JM8006)
陕西省教育厅专项科研项目(2010JK620)
关键词
机械领域
灰色关联分析
贝叶斯分类器
特征选择
machinery-oriented
grey relation analysis
Bayesian classifier
feature selection
