Improved churn prediction in telecommunication industry using data mining techniques

Keramati, Abbas; Jafari-Marandi, Ruholla; Aliannejadi, Mohammad; Ahmadian, I.; Mozaffari, Mona; Abbasi, Ubaid

doi:10.1016/j.asoc.2014.08.041

Cited by 137 publications

(97 citation statements)

References 42 publications

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“…Obviously the class attribute is Churn [12]- [20]. Looking into data, we saw there were 495 records with the class label churned and the rest, i.e.…”

Section: International Journal Of Future Computer and Communication mentioning

confidence: 99%

Addressing Churn Prediction Problem with Meta-Heuristic, Machine Learning, Neural Network and Data Mining Techniques: A Case Study of a Telecommunication Company

Keramati¹,

Marandi²

2015

IJFCC

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Abstract-In order to survive in today telecommunication market it is essential to have the ability of distinguishing customers who are probable to switch into a competitor. Customer churn prediction is a means to address the complication and has become an important issue in Telecommunication business. In such competitive market a reliable means to predict customer future's action would be regarded as priceless. To end, this paper has employed Meta-heuristic, Machine learning, Neural Network and data mining techniques including Genetic Algorithm, Particle Swarm Optimization, Support Vector Machine, Artificial Neural Networks, Decision Tree, and K-Nearest Neighbors so as to solve churn prediction problem. Using the data of an Iranian mobile company not only these techniques were experienced and were compared to each other, but also we drawn a parallel between some different prominent data mining software. However, the result indicates that this paper ANN performed the best with near 90 percent precision and recall.

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“…Obviously the class attribute is Churn [12]- [20]. Looking into data, we saw there were 495 records with the class label churned and the rest, i.e.…”

Section: International Journal Of Future Computer and Communication mentioning

confidence: 99%

Addressing Churn Prediction Problem with Meta-Heuristic, Machine Learning, Neural Network and Data Mining Techniques: A Case Study of a Telecommunication Company

Keramati¹,

Marandi²

2015

IJFCC

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“…Farquad [3], A. Rodan [4], Huang Bingquan [5], T. Vafeiadis [6], Huang Ying and T. Kechadi [7], A. Keramati [8] et al In the earlier stages of our research, we have studied the following basic methods used for binary classification: Decision Trees, k-Nearest Neighbors, Support Vector Machines, and Back-Propagation Artificial Neural Networks. We have evaluated model quality over different sets of parameters, such as the number of neurons in a hidden layer in artificial neural networks (Fig.…”

Section: Previous Research and Problem Statementmentioning

confidence: 99%

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction

Alkhasov¹,

Tselykh²

2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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“…In the last few decades, various techniques have been successfully proposed to solve classification problems in the fields of machine learning and data mining [1][2][3]. However, most of the existing classification techniques are designed to handle data with binary or nominal class labels (where class labels are independent).…”

Section: Introductionmentioning

confidence: 99%

Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

Liang¹,

Guo²,

Sun³

et al. 2017

Mathematical Problems in Engineering

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Ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named ℎ AntMiner order , for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.

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Improved churn prediction in telecommunication industry using data mining techniques

Cited by 137 publications

References 42 publications

Addressing Churn Prediction Problem with Meta-Heuristic, Machine Learning, Neural Network and Data Mining Techniques: A Case Study of a Telecommunication Company

Addressing Churn Prediction Problem with Meta-Heuristic, Machine Learning, Neural Network and Data Mining Techniques: A Case Study of a Telecommunication Company

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction

Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

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