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

Abstract

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.

Keywords

• software railway safety problems
• tf-idf
• base classifier
• integrated classifier
• evolutionary integration classifier