Applied multivariate techniques
Applied multivariate techniques
Machine Learning
Neural networks in applied statistics
Technometrics
User profiling in personalization applications through rule discovery and validation
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of fraud rules for telecommunications—challenges and solutions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Just the fax—differentiating voice and fax phone lines using call billing data
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Emerging scientific applications in data mining
Communications of the ACM - Evolving data mining into solutions for insights
Journal of Parallel and Distributed Computing - Special issue on wireless networks
Data Mining and Knowledge Discovery
Signature-Based Methods for Data Streams
Data Mining and Knowledge Discovery
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts
IEEE Expert: Intelligent Systems and Their Applications
How to Increase Security in Mobile Networks by Anomaly Detection
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
An extended self-organizing map network for market segmentation: a telecommunication example
Decision Support Systems
Expert Systems with Applications: An International Journal
Employing Latent Dirichlet Allocation for fraud detection in telecommunications
Pattern Recognition Letters
Computational Statistics & Data Analysis
Searching customer patterns of mobile service using clustering and quantitative association rule
Expert Systems with Applications: An International Journal
Boosting and measuring the performance of ensembles for a successful database marketing
Expert Systems with Applications: An International Journal
Estimating the utility value of individual credit card delinquents
Expert Systems with Applications: An International Journal
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Issues in stacked generalization
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Computational Statistics & Data Analysis
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
Hybrid mining approach in the design of credit scoring models
Expert Systems with Applications: An International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Network intrusion and fault detection: a statistical anomaly approach
IEEE Communications Magazine
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
A probabilistic approach to fraud detection in telecommunications
Knowledge-Based Systems
Early security classification of skype users via machine learning
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
A survey of multiple classifier systems as hybrid systems
Information Fusion
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Service providing companies including telecommunication companies often receive substantial damage from customers' fraudulent behaviors. One of the common types of fraud is subscription fraud in which usage type is in contradiction with subscription type. This study aimed at identifying customers' subscription fraud by employing data mining techniques and adopting knowledge discovery process. To this end, a hybrid approach consisting of preprocessing, clustering, and classification phases was applied, and appropriate tools were employed commensurate to each phase. Specifically, in the clustering phase SOM and K-means were combined, and in the classification phase decision tree (C4.5), neural networks, and support vector machines as single classifiers and bagging, boosting, stacking, majority and consensus voting as ensembles were examined. In addition to using clustering to identify outlier cases, it was also possible - by defining new features - to maintain the results of clustering phase for the classification phase. This, in turn, contributed to better classification results. A real dataset provided by Telecommunication Company of Tehran was applied to demonstrate the effectiveness of the proposed method. The efficient use of synergy among these techniques significantly increased prediction accuracy. The performance of all single and ensemble classifiers is evaluated based on various metrics and compared by statistical tests. The results showed that support vector machines among single classifiers and boosted trees among all classifiers have the best performance in terms of various metrics. The research findings show that the proposed model has a high accuracy, and the resulting outcomes are significant both theoretically and practically.