基于进化集成分类器的铁路安全隐患智能分类

Intelligent Classification of Railway Safety Hazards Based on Evolutionary Ensemble Classifier

针对铁路安全事故隐患文本数据分类提出进化集成分类器模型。分析安全事故隐患数据特征,根据每一类安全事故隐患数据都有特征关键词的特点,运用TF-IDF方法提取文本特征并转换为向量。设计进化集成分类器模型实现流程。采用Bagging集成分类器将TF-IDF转换后的文本向量进行随机采样,训练若干个决策树基分类器模型,设计遗传算法编码机制、灵敏度设定、适应度函数及目标函数选择等关键步骤。根据遗传算法流程实现基分类器组合优化,将经过遗传算法进化的最优个体对应的基分类器参与Bagging投票分类,验证分类效果。通过对某铁路局供电接触网安全事故隐患文本数据实验分析,进化集成分类器模型在安全事故隐患分类的准确率相比于单个决策树分类器和Bagging集成分类器分类结果分别提升17.42%和4.63%,证明设计的进化集成分类模型能够取得较好的分类效果,可应用于铁路安全事故隐患分类。

An evolutionary ensemble classifier model is proposed for text data classification of railway safety hazards.Based on analyses of characteristics of the data of safety accidents,TF IDF method is used to extract features of text and converted into vectors.Implementation processes of the evolutionary ensemble classifier model are designed.Bagging ensemble classifier is applied for random sampling of the text vector,and a number of decision tree classifier models are trained.A genetic algorithm is used for coding mechanism,sensitivity setting,fitness function,and key steps of selecting target function.According to the process of genetic algorithm,combination optimization for base classifier is achieved.The base classifier corresponds with the optimal evolution from genetic algorithm is used in Bagging voting classification to verify effects.Based on a case study of text data from a Railway Bureau,compared with decision tree classifier and Bagging ensemble classifier,classification accuracy of the model is improved by 17.42%and 4.63%,respectively.The results show that the model has better classification effects and can be applied to classify railway safety hazards.