Ensemble of Example-Dependent Cost-Sensitive Decision Trees

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio

The Role of Peer Influence in Churn in Wireless Networks

Subscriber churn remains a top challenge for wireless carriers. These carriers need to understand the determinants of churn to confidently apply effective retention strategies to ensure their profitability and growth. In this paper, we look at the effect of peer influence on churn and we try to disentangle it from other effects that drive simultaneous churn across friends but that do not relate to peer influence. We analyze a random sample of roughly 10 thousand subscribers from large dataset from a major wireless carrier over a period of 10 months. We apply survival models and generalized propensity score to identify the role of peer influence. We show that the propensity to churn increases when friends do and that it increases more when many strong friends churn. Therefore, our results suggest that churn managers should consider strategies aimed at preventing group churn. We also show that survival models fail to disentangle homophily from peer influence over-estimating the effect of peer influence.Comment: Accepted in Seventh ASE International Conference on Social Computing (Socialcom 2014), Best Paper Award Winne

A predict-and-optimize approach to profit-driven churn prevention

In this paper, we introduce a novel predict-and-optimize method for profit-driven churn prevention. We frame the task of targeting customers for a retention campaign as a regret minimization problem. The main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast, many profit-driven strategies focus on churn probabilities while considering average CLVs. This often results in significant information loss due to data aggregation. Our proposed model aligns with the guidelines of Predict-and-Optimize (PnO) frameworks and can be efficiently solved using stochastic gradient descent methods. Results from 12 churn prediction datasets underscore the effectiveness of our approach, which achieves the best average performance compared to other well-established strategies in terms of average profit.Comment: 15 pages, 4 figures, submitted to INFORMATION SCIENCE

A Dynamic Classification Approach to Churn Prediction in Banking Industry

Churn prediction is the process of using transaction data to identify customers who are likely to cease their relationship with a company. To date, most work in churn prediction focuses on sampling strategies and supervised modeling over a short period of time. Few have explored the area of mining customer behavior pattern in longitudinal data. This research developed a dynamic approach to optimizing model specifications by using time-series predictors, multiple time periods, and rare event detection to enable accurate churn prediction. The study used a unique three-year dataset consisting of 32,000 transaction records of a retail bank in Florida, USA. It uses trend modeling to capture the change of customer behavior over time. Results show that data from multiple time periods helped to improve model precision and recall. This dynamic churn prediction approach can be generalized to other fields for which mining long term customer data is necessary

Data Mining Technique for Predicting Telecommunications Industry Customer Churn Using both Descriptive and Predictive Algorithms

As markets have become increasingly saturated, companies have acknowledged that their business strategies need to focuson identifying those customers who are most likely to churn. It is becoming common knowledge in business, that retainingexisting customers is the best core marketing strategy to survive in industry. In this research, both descriptive and predictivedata mining techniques were used to determine the calling behaviour of subscribers and to recognise subscribers with highprobability of churn in a telecommunications company subscriber database. First a data model for the input data variablesobtained from the subscriber database was developed. Then Simple K-Means and Expected Maximization (EM) clusteringalgorithms were used for the clustering stage, while Decision Stump, M5P and RepTree Decision Tree algorithms were usedfor the classification stage. The best algorithms in both the clustering and classification stages were used for the predictionprocess where customers that were likely to churn were identified.Keywords: customer churn; prediction; clustering; classificatio

Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifie