A Stacking-Based Ensemble Framework for Anomaly Detection in High-Dimensional Data

Research suggests that return and refund fraud poses a significant challenge to the fashion e-commerce, leading to financial losses and operational inefficiency. Traditional anomaly detection methods struggle with class imbalance and inconsistency across a single model approach. In this work, we propose a novel Hybrid Stacking Ensemble Framework which integrates Isolation Forest, One-Class Support Vector Machine (OCSVM), and Autoencoder with a Logistic Regression meta-classifier. To address the severe imbalance in the dataset, SMOTE was used to generate synthetic samples for minority class. Experiment demonstrated that while baseline imbalanced dataset achieved an accuracy of 76% and F1-score of 0.76, the resulted dataset with SMOTE achieved approximately 0.92 F1-score. This paper contributes a hybrid ensemble and data augmentation pipeline that can serve as a foundation for suture anomaly detection research.