朴素Bayes分类器文本特征向量的参数优化

采用朴素Bayes算法建立中文文本自动分类器, 并研究相关参数的选择问题, 以实现中文文本的高效分类. 首先在模型训练阶段, 采用N-gram模型处理训练数据集提取特征向量; 然后使用朴素Bayes算法建立文本分类器; 最后在模型测试阶段, 为提高分类准确率, 使用词频反文档频率算法对测试样本进行特征向量提取. 实例分析结果表明, 在提取训练集特征向量时, 2-gram模型和4-gram模型的特征提取效果最佳; 在选取特征向量长度时, 长度为25 000的特征向量可使分类准确率出现最大增幅并保证较高准确率; 在确定特征项词性方面, 同时选取动词和名词可使分类器准确率达到最高, 仅选取动词时准确率最低.

Parameter Optimization of Text Feature Vectorof Nave Bayesian Classifier

Nave Bayesian algorithm was used to build an automatic Chinese text classifier, and the selection of relevant parameter was studied to realize the efficient classification of Chinese text. Firstly, in model training stage, N-gram model was used to extract feature vectors from training data sets. Secondly, Nave Bayesian algorithm was used to build a text classifier. Finally, in model testing stage, in order to improve the classification accuracy, term frequency inverse document frequency algorithm was used to extract feature vectors of the test samples. The results show that when extracting feature vectors from training sets, 2-gram model and 4-gram model have the best effect of feature extraction; when selecting the length of feature vectors, the length of 25 000 can make the greatest increment of classification accuracy and ensure a higher accuracy; when determining the characteristic of feature items, the accuracy is the highest when both verbs and nouns are selected, and the lowest when only verbs are selected.