C4.5: programs for machine learning
C4.5: programs for machine learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The nature of statistical learning theory
The nature of statistical learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Future Generation Computer Systems
Automated Cellular Modeling and Prediction on a Large Scale
Artificial Intelligence Review - Issues on the application of data mining
Credit Scoring and Its Applications
Credit Scoring and Its Applications
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Genetic Modelling of Customer Retention
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Ant-Based Clustering and Topographic Mapping
Artificial Life
Computers and Operations Research
Expert Systems with Applications: An International Journal
A neural clustering and classification system for sales forecasting of new apparel items
Applied Soft Computing
Mining software repositories for comprehensible software fault prediction models
Journal of Systems and Software
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
Predicting going concern opinion with data mining
Decision Support Systems
Finding groups in data: Cluster analysis with ants
Applied Soft Computing
A modified Pareto/NBD approach for predicting customer lifetime value
Expert Systems with Applications: An International Journal
Decompositional Rule Extraction from Support Vector Machines by Active Learning
IEEE Transactions on Knowledge and Data Engineering
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Inferring comprehensible business/ICT alignment rules
Information and Management
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Expert Systems with Applications: An International Journal
Ant-based approach to the knowledge fusion problem
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Identifying financially successful start-up profiles with data mining
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Churn prediction in new users of Yahoo! answers
Proceedings of the 21st international conference companion on World Wide Web
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
Performance of classification models from a user perspective
Decision Support Systems
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
Functional Link Artificial Neural Networks for Software Cost Estimation
International Journal of Applied Evolutionary Computation
ABML knowledge refinement loop: a case study
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
International Journal of Information Retrieval Research
An interpretable classification rule mining algorithm
Information Sciences: an International Journal
Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines
Expert Systems with Applications: An International Journal
Social network analysis for customer churn prediction
Applied Soft Computing
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
Profit optimizing customer churn prediction with Bayesian network classifiers
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
Hi-index | 12.06 |
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predictive accuracy, comprehensibility, and justifiability are three key aspects of a churn prediction model. An accurate model permits to correctly target future churners in a retention marketing campaign, while a comprehensible and intuitive rule-set allows to identify the main drivers for customers to churn, and to develop an effective retention strategy in accordance with domain knowledge. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It is shown that only limited attention has been paid to the comprehensibility and the intuitiveness of churn prediction models. Therefore, two novel data mining techniques are applied to churn prediction modeling, and benchmarked to traditional rule induction techniques such as C4.5 and RIPPER. Both AntMiner+ and ALBA are shown to induce accurate as well as comprehensible classification rule-sets. AntMiner+ is a high performing data mining technique based on the principles of Ant Colony Optimization that allows to include domain knowledge by imposing monotonicity constraints on the final rule-set. ALBA on the other hand combines the high predictive accuracy of a non-linear support vector machine model with the comprehensibility of the rule-set format. The results of the benchmarking experiments show that ALBA improves learning of classification techniques, resulting in comprehensible models with increased performance. AntMiner+ results in accurate, comprehensible, but most importantly justifiable models, unlike the other modeling techniques included in this study.