Abstract
In the field of business data analysis, customer churn prediction analysis plays an important role. This paper combines traditional statistical prediction methods and artificial intelligence prediction methods to propose a customer churn prediction analysis model based on missing data in an attempt to explore a new solution in this field. Based on the missing data in this model, factor analysis method and data mining technique are used to generate key factor sets and their values to form input neurons and their initial values. The number of hidden layer neurons was determined by combinatorial prediction. Using the improved genetic algorithm, the initial weight and threshold of BP network are determined. Finally, the prediction results and key attribute data related to the prediction results are generated for decision makers to analyze the problem. The experiment evaluates the model from the aspects of accuracy, precision, recall, and f-measure, which proves that the model is effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barros, R.C., Basgalupp, M.P., Freitas, A.A., et al.: Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets. IEEE Trans. Evol. Comput. 18(1), 873–892 (2014)
Dong, Y., Guo, H., Zhi, W., Fan, M., et al.: Class imbalance oriented logistic regression. In: Cyber-Enabled Distributed Computing and Knowledge Discovery, Shanghai, pp. 187–192 (2014)
Tang, L., Liu, H.: Bias analysis in text classification for highly skewed data. In: Fifth IEEE International Conference on Data Mining, New York, p. 4 (2005)
Zhang, Z., Cheng, H.: Clustering aggregation based on genetic algorithm for documents clustering. In: IEEE World Congress on Computational Intelligence, Hong Kong, pp. 3156–3161 (2008)
Immon, W.H.: Building the data warehouse.
(2003)Louis, A.C.: Data mining and causal modeling of customer behaviors. Telecommun. Syst. 21(2–4), 349–358 (2002). https://doi.org/10.1023/A:1020911018130
Chum modeling for mobile telecommunications. http://docs.salford-systems.com/--chumwinF08.pdf
Ning, Y., Zheng, X.: Notice of retraction forecasting the natural forest stand age based on artificial neural network model. In: Computer and Communication Technologies in Agriculture Engineering, Chengdu, pp. 536–539 (2010)
Yang, Z., Shi, X.: An agent-based immune evolutionary learning algorithm and its application. In: Intelligent Control and Automation, Shenyang, pp. 5008–5013 (2014)
Chaudhuri, A., De, K.: A study of the traveling salesman problem using fuzzy self organizing map. In: International Conference on Industrial and Information Systems, Kharagpur, pp. 1–5 (2008)
Zhang, Y.: Cluster-based majority under-sampling approaches for class imbalance learning. In: IEEE International Conference on Information and Financial Engineering, Chongqing, 400–404 (2010)
Beigy, H., MeybodiM, R.: A learning automata-based algorithm for determination of the number of hidden units for three-layer neural networks. Int. J. Syst. Sci. 40(1), 101–118 (2009)
Liu, B., Guo, HX.: Matlab Neural Network Super Learning Manual. People’s Posts Telecommun. Publishing House, BeiJing (2014)
Ahmed, A.A., Maheswari, D.: Churn prediction on huge telecom data using hybrid firefly based classification. Egypt. Inform. J. 18(3), 215–220 (2017)
Rizopoulos, D., Kuhn, M., Johnson, K.: Applied predictive modeling. Biometrics 74(1), 383–393 (2018)
(2003)Acknowledgment
This work was financially supported by the national natural science foundation of China (61561055).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zeng, R., Yuan, L., Ye, Z., Cai, J. (2020). Prediction and Analysis Model of Telecom Customer Churn Based on Missing Data. In: Dong, D., Gong, X., Li, C., Li, D., Wu, J. (eds) Advanced Computer Architecture. ACA 2020. Communications in Computer and Information Science, vol 1256. Springer, Singapore. https://doi.org/10.1007/978-981-15-8135-9_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-8135-9_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8134-2
Online ISBN: 978-981-15-8135-9
eBook Packages: Computer ScienceComputer Science (R0)
