摘要
提出一种基于多重假设检验的特征加权朴素贝叶斯分类算法,该算法通过特征选择方法得到多个特征词集合,再按多重假设检验错误率为每个特征词集合配以不同的权重系数并参与到分类器的构建中.该方法已经应用到市长公开电话的文本分类中,通过构建的3个特征加权朴素贝叶斯分类器实现了投诉文本的计算机自动分类,且相对传统方法提高了分类器的效率和精度.
On the basis of multiple hypothesis testing, we proposed a feature weighted naive Bayesian algorithm, which outputs many sets of feature words by means of feature selection, and assigns a coefficient to each set of feature words which is used to construct the classifier in terms of the error rate of multiple hypothe- sis testing. This algorithm was used in the text classification of the mayor' s public access line project, where we realized the automatic classification of complaint texts by constructing three feature weighted naive Bayesian classifiers. Compared with those of the traditional methods, the efficiency and accuracy of our classifier are higher
出处
《吉林大学学报(理学版)》
CAS
CSCD
北大核心
2008年第6期1101-1104,共4页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:10571073)
关键词
多重假设检验
文本分类
特征加权
市长公开电话
multiple hypothesis testing
text classification
feature weighted
the mayor' s public access line project