Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications

Huang, B. Q.; Buckley, Brian; Kechadi, Tahar

doi:10.1016/j.eswa.2009.10.027

Cited by 127 publications

(58 citation statements)

References 18 publications

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“…Companies go this length because the cost of retaining an existing customer is far less than acquiring a new one [4]. The telecommunications industry gives special attention to this problem [5], [6], [7], [8], [9], [10], [11], [12]. This is due to the low barriers involved in switching service providers.…”

Section: B Studies On Churn Ratesmentioning

confidence: 99%

Modeling Attrition in Organizations from Email Communication

Patil

Long

Shen

et al. 2013

2013 International Conference on Social Computing

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Abstract-Modeling people's online behavior in relation to their real-world social context is an interesting and important research problem. In this paper, we present our preliminary study of attrition behavior in real-world organizations based on two online datasets: a dataset from a small startup (40+ users) and a dataset from one large US company (3600+ users). The small startup dataset is collected using our privacy-preserving data logging tool, which removes personal identifiable information from content data and extracts only aggregated statistics such as word frequency counts and sentiment features. The privacypreserving measures have enabled us to recruit participants to support this study. Correlation analysis over the startup dataset has shown that statistically there is often a change point in people's online behavior, and data exhibits weak trends that may be manifestation of real-world attrition. Same findings are also verified in the large company dataset. Furthermore, we have trained a classifier to predict real-world attrition with a moderate accuracy of 60-65% on the large company dataset. Given the incompleteness and noisy nature of data, the accuracy is encouraging.

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Section: B Studies On Churn Ratesmentioning

confidence: 99%

Modeling Attrition in Organizations from Email Communication

Patil

Long

Shen

et al. 2013

2013 International Conference on Social Computing

View full text Add to dashboard Cite

show abstract

“…Adem Karahoca et al [8] proposed a data mining solution with a neural network model to predict churners. The x-means and fuzzy c-means algorithm were used for feature selection and used ANFIS learning algorithm for churn prediction.…”

Section: Data Mining Techniques For Churn Detectionmentioning

confidence: 99%

Survey of Data Mining Techniques Used for Real Time Churn Prediction

Turiho¹,

Cheruiyot²,

Kibe³

et al. 2017

IJARCSSE

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Customer churn is perhaps the biggest challenge in telecommunication industry. Customer churn is the term which indicates the customer who is in the stage to leave the company or Customer churn means a customer can leave their service or service provider and move to another service or service provider. This rate is increasing day by day in all telecommunication companies. Data mining is one of the techniques which provide different methods and application to find out those customers who are going to churn and how to prevent them. The aim of this paper is to compare different algorithms of Data Mining which have been used for making distinction between customers into non churn and churn, so that appropriate steps can be taken into consideration in order to retain the churn customers to the company as customers are more valuable to the survival and development of the company.

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“…Tseng and Huang [39] introduced rough set theory (RST) to feature selection for predicting customer purchasing behavior. Huang et al [17] applied a new multi-objective feature selection approach to churn prediction in telecommunications service field. Chen and Li [8] utilized SVM classifier combinined with conventional statistical LDA, Decision tree, rough sets and F-score approaches as a feature pre-processing step to discriminate between good and bad customers in credit scoring.…”

Section: Feature Selection and Class Imbalancementioning

confidence: 99%

A new transferred feature selection algorithm for customer identification

Zhu¹,

Niu²,

Xiao³

et al. 2016

Neural Comput & Applic

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Class imbalance brings great challenges to feature selection in customer identification and most of current feature selection approaches cannot produce good prediction on the minority class. A number of studies have attempted to solve this issue by using resampling techniques. However, resampling techniques only use the in-domain information and they cannot achieve good performance when the imbalance is caused by the absolute rarity of the minority class. In this paper, we focus on the issue of feature selection with class imbalance caused by absolute rarity. By introducing the idea of transfer learning, we develop a transferred feature selection method based on the group method of data handling neural network (GMDH). The proposed ensemble neural network extracts information of similar customers from related domains to deal with the information scarcity of the minority class in the target domain. Experiments are done on a real-world application from cigarette company. The results indicate that the new method gives better predictive performance than other benchmark feature selection methods, especially the predictive accuracy of minority high-value customers. At the same time, the new algorithm can help to identify important features that distinguish highvalue customers from low-value ones.

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Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications

Cited by 127 publications

References 18 publications

Modeling Attrition in Organizations from Email Communication

Modeling Attrition in Organizations from Email Communication

Survey of Data Mining Techniques Used for Real Time Churn Prediction

A new transferred feature selection algorithm for customer identification

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