Qingqing Chang
ABSTRACT
Recent years witnesses the rampancy of telephone fraud along with the development of modern communication technology. The challenges from telephone fraud identification mainly exist in two aspects: (1) the telephone fraud records are typical imbalanced data due to the characteristic of heterogeneous spatial-temporal distribution, leading to bias towards predicting the majority class; (2) traditional evaluation metrics in imbalanced learning mainly rely on accuracy or precision, neglecting the completeness of telephone fraud identification in real-world implementations.
In response to the limitations of traditional methods, we propose the Stacked-SVM framework based on heterogeneous ensemble learning and support vector machines (SVMs). We first employ both edited nearest neighbors (ENN) and adaptive synthetic sampling (ADASYN) to alleviate the high dimensional curse in imbalanced data resampling; secondly, we propose the optimal linear combination strategy in the iteration of Stacked-SVM and demonstrate its validity with the help of Kullback-Leibler divergence. Finally, we construct the Stacked-SVM framework with respect to the constraints of the loss function in SVM. We further compare the performance under different evaluation metrics (i.e., accuracy, precision, recall, F1-score, and AUC value) with other four traditional telephone fraud identification methods, namely Logistic Regression, Isolation Forest, SVM with random parameter settings, and optimized SVM.
We implement Stacked-SVM with a list of experiments based on real telephone fraud data sets in the form of calling detail records (CDRs) from a Chinese domestic telecom operator. The experimental results show that the proposed Stacked-SVM holds a 93.83% recall value and an 82.96% accuracy in telephone fraud identification, behaving more precise and robust than other models.
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Index Terms
- Stacked-SVM: A Dynamic SVM Framework for Telephone Fraud Identification from Imbalanced CDRs
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