Abstract
With increasingly fierce competition in other transportation markets, the customer churn in railway freight becomes a very serious problem. Facing the high similarity and indistinguishability of railway freight data, the customer churn prediction (CCP) becomes one of the challenging tasks in this industry. In this paper, a deep forest-based model is developed which can achieve better accuracy of churn predicting in railway freight customer and effectively separate the churners from the non-churners. Inspired by the layer-by-layer processing of deep neural network, the cascade structure is adopted to the deep forest, in which the input of each layer includes the original features and the feature information processed by the previous layer. The deep forest in this paper achieves the best performance in railway freight data with the accuracy of 0.78, the precision of 0.75, the recall of 0.66, the F1-score of 0.7 and the AUC value of 0.86, which are higher than those of decision tree, AdaBoost and XGBoost. At the same time, this model has smaller standard error and makes more stable performance.
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Liu, D., Zhang, X., Shi, Y., Li, H. (2021). Prediction of Railway Freight Customer Churn Based on Deep Forest. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_40
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