基于TF-IDF加权朴素贝叶斯算法的ATP车载设备测试案例分类研究

针对列车超速防护（ATP,Automatic Train Protection）系统车载设备测试案例分类存在的工作量大、效率低且准确性不高等问题，提出了将词频—逆文档频率（TF-IDF,Term Frequency-Inverse Document Frequency）与朴素贝叶斯算法相结合，应用于测试案例分类的方案。利用TF-IDF算法筛选特征词及权重，对朴素贝叶斯算法进行加权处理，并基于实验室现有ATP车载设备的测试案例进行验证。实验结果表明，文章的特征词提取及测试案例分类方法具有较高的准确性。

Classification of ATP on-board equipment test cases based on TF-IDF weighted Naive Bayesian algorithm

Aiming at the problems of heavy workload, low efficiency and low accuracy in the classification of test cases of on-board equipment of ATP (Automatic Train Protection) system, this paper proposed a scheme that combined TF-IDF (Term Frequency Inverse Document Frequency) with Naive Bayesian algorithm to classify test cases. The paper used TF-IDF algorithm to filter feature words and weights, and weighted Naive Bayesian algorithm, which was verified based on the test cases of existing ATP on-board equipment in the laboratory. The experiment results show that the method of feature word extraction and test case classification has high accuracy.