计算机应用与软件Issue(9)：253-255,3.DOI:10.3969/j.issn.1000-386x.2014.09.063
基于加权补集的朴素贝叶斯文本分类算法研究
RESEARCH ON WEIGHTED COMPLEMENT-BASED NAIVE BAYES TEXT CLASSIFICATION ALGORITHM
摘要
Abstract
Naive Bayes classification method is widely used in text classification filed because of its simple and fast characteristics.Butwhen the distribution of sample data in training set of each class is uneven,the classification accuracy of Naive Bayes classifier is less thanideal.To solve this problem,we propose a weighted complement-based Naive Bayes text classification algorithm.It uses the features ofcomplementary set of a certain category to represent the features of current categories,and normalises the weight of the features.Comparisonhas been made through experiment in regard to the influence on the effect of text classification by this method and by traditional Naive Bayesmethod,the experimental results show that the weighted complement-based Naive Bayes algorithm has better text classification effect.关键词
文本分类/朴素贝叶斯/补集/权重Key words
Text classification/Naive Bayes/Complement/Weight分类
信息技术与安全科学引用本文复制引用
杜选..基于加权补集的朴素贝叶斯文本分类算法研究[J].计算机应用与软件,2014,(9):253-255,3.基金项目
浙江省自然科学基金项目（Y12F020128）。 （）
