Naif Almusallam1,*, Junaid Qayyum2,3
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3653-3687, 2025, DOI:10.32604/cmc.2025.067220
- 23 September 2025
Abstract This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection, unsupervised clustering, and ensemble learning to improve classification performance in financial transaction monitoring systems. The framework is structured into three core layers: (1) feature selection using Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and Mutual Information (MI) to reduce dimensionality and enhance input relevance; (2) anomaly detection through unsupervised clustering using K-Means, Density-Based Spatial Clustering (DBSCAN), and Hierarchical Clustering to flag suspicious patterns in unlabeled data; and (3) final classification using a voting-based hybrid ensemble of Support Vector Machine (SVM),… More >
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