基于TF-IDF进化集成分类器的铁路安全故障文本分类

Text classification of railway safety faultbased on TF-IDF evolutionary integrated classifier

铁路安全问题是铁路运输保证的核心问题,铁路安全问题非结构化文本数据量大,文本内容无特定规律,对于综合分析解决安全故障问题造成很高的难度。针对铁路安全问题数据智能分类,提出进化集成分类器模型。运用TF-IDF算法,通过分析接触网安全问题的数据特点提取文本特征。采用决策树作为基分类器的Bagging集成分类器将文本数据分类,在Bagging分类过程中,针对Bagging算法产生的基分类器组合解集,提出采用遗传算法(Genetic Algorithm)将其优化,产生分类结果较优的基分类器组合解集。以铁路局实际安全问题进行实验分析,实验证明TF-IDF+Bagging+Genetic Algorithm=Evolutionary Ensemble Classifier进化集成分类器模型在铁路安全问题文本分类中具有较高的准确性。

Railway safety is the core of railway transportation guarantee.The unstructured text data of railway safety problems is large,and the content of the text has no specific rules,which makes it very difficult to comprehensively analyze and solve the safety problems.Aiming at the intelligent classification of railway safety data,an evolutionary ensemble classifier model is proposed.By analyzing the characteristics of the catenary security issues of data,TF-IDF model is adopted to realize the feature extraction.Bagging ensemble classifier which uses Decision Tree as the base classifier classifies the text data,in the process of classification of Bagging,for the combined solution set of base classifier generated by Bagging Algorithm,Genetic Algorithm is proposed to optimize it to generate the combined solution set of base classifier with better classification results.Based on the safety problem of power supply contact network of a railway bureau,the experimental analysis shows that the TF-IDF+Bagging+Genetic Algorithm=Evolutionary Ensemble Classifier model has a high classification index in the text classification of railway safety problems.