A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Enhancements to the data mining process
Enhancements to the data mining process
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
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data Mining and Knowledge Discovery
Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Testing the fraud detection ability of different user profiles by means of FF-NN classifiers
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Telecommunications fraud has drawn the attention in research due to the huge economic burden on companies and to the interesting aspect of users' behavior modeling. In the present paper, an application of decision trees to fraud detection is presented. The appropriate user behavior modeling is, also, discussed. The trees are used for rule extraction in order to distinguish between normal and fraudulent activities in a telecommunications network. Several real cases of defrauded user accounts are modeled by means of selected usage features. Decision trees are applied in order to identify the critical values that separate fraud from legal use. These thresholds are expressed in the form of rules that will be used in a rule based expert system.