Earticle

초록

영어

This paper presents a novel hybrid data drift detection framework, the Ensemble Modeling-based Hybrid Drift Score (EM-HDS), that integrates Principal Component Analysis (PCA), Variational Autoencoders (VAEs), and ensemble modeling to effectively detect both global structural shifts and localized non-linear variations in feature space. PCA identifies global changes by monitoring variance alignment and principal component transformations, while VAEs enhance sensitivity to localized anomalies through probabilistic modeling of reconstruction errors. The EM-HDS framework integrates complementary techniques using a Random Forest ensemble model, effectively capturing complex, non-linear relationships between PCA and VAE metrics. Experimental evaluations on synthetic datasets with simulated drift and real-world COCO image features demonstrate the robustness and adaptability of the proposed method. EM-HDS delivers superior drift detection performance, significantly improving detection accuracy, particularly in scenarios involving simultaneous global and local drifts, surpassing standalone PCA and VAE approaches. Although the framework requires careful tuning of hyperparameters to adapt to specific datasets, its ability to dynamically adjust to diverse drift patterns makes it a practical and effective solution for real-time monitoring and adaptation in dynamic environments. This research establishes a strong foundation for enhancing the reliability of machine learning models in complex, real-time applications for future work.

목차

Abstract
1. INTRODUCTION
2. RELATED WORK
3. METHODOLOGY
3.1 PCA-Based Drift Detection
3.2 Autoencoder-Based Drift Detection
3.3 Hybrid Drift Score (HDS)
3.4 Variational Autoencoder for Local Drift Detection
3.5 Ensemble Modeling-based Hybrid Drift Score (EM-HDS)
3.6 Dynamic Weighting of EM-HDS
4. EXPERIMENTAL SETUP AND SIMULATION RESULTS
5. CONCLUSION
6. REFERENCE

키워드

저자

• Yeonwoo Lee [ Professor, Department of Artificial Intelligence Engineering, Mokpo National University, Chonnam, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380