基于改进权重贝叶斯的维文文本分类模型

Uyghur text classification model based on improved weighted Bayes

为提高朴素贝叶斯分类器的分类性能,考虑决策分类过程中条件属性的不同重要程度,提出了一种基于特征选择权重的贝叶斯分类算法。采用卡方值和文档频数相结合的数值来表示特征词的重要程度,对该值进行处理获得每个特征词权重,建立加权贝叶斯分类器。在研究维文特点的基础上,利用该算法构建了一个维文文本分类模型。在搜集到的维文语料库上进行的实验结果表明,该算法比朴素贝叶斯拥有更好的分类性能。

To improve the performance of the Naive Bayes classifier, a weighted Bayes method is proposed based on feature selec- tion weight for taking into account different conditions have different effects to the decision conditions. Firstly, the effect value of every feature is computed by the combination of the Chi Square value and document frequency, DF. Then, the weight of every feature is computed by the effect value and weighted Bayes Classifier is built. A Uyghur text classification model is built based on the properties of Uyghur Language and this method. Results of the experiment based on the Uyghur corpus which collected from the internet indicate the metric had better classification performance than Naive Bayesian Classifier.