Intelligent classification model for railway signal equipment fault based on SMOTE and ensemble learning
Lianbao Yang, Ping Li, Rui Xue, Xiaoning Ma, Xinqin Li, Zhe Wang · IOP Conference Series Materials Science and Engineering · 2018
In this paper, we propose a novel intelligent classification model to classify the railway signal equipment fault based on SMOTE and ensemble learning. To tackle the imbalanced fault text data, the model uses SMOTE algorithm to generate the minority railway signal equipment fault class data randomly, making the data balanced. Then the model adopts the base classifier, such as Logistic Regression, Multinomial Naive Bayes, SVM and the ensemble classifier, such as GBDT, Random Forests to classify the data processed by SMOTE. To combine the advantages of various classifiers, the model integrates multiple classifiers by way of voting. Based on the experiment analysis of railway signal equipment fault text data from 2012 to 2016, the result shows that the model has a significant improvement in fault classification accuracy, recall rate and f-score.