Purely automated machine learning systems often struggle to incorporate domain knowledge and contextual reasoning, resulting in reduced performance when handling ambiguous, noisy, or complex real-world data. Although existing approaches such as active learning and semi-supervised learning partially address these limita...
Isnawijayani, E. Noche, Yamini Sood et al.· Journal of Data Science· 0 citations
The results demonstrate that data-centric interventions consistently outperform model-centric improvements, with the proposed approach achieving up to 95% accuracy, significantly surpassing baseline models trained on raw data.
Nia Oktaviani, E. Noche, S. Patil et al.· Journal of Data Science· 0 citations
A systematic comparative framework that integrates data-level resampling, cost-sensitive learning, and hybrid approaches to evaluate their performance under varying imbalance ratios and noise levels indicates that hybrid approaches consistently outperform standalone methods, achieving the most stable and balanced perfo...
Tamsir Ariyadi, E. Noche, Nisha Pandey et al.· Journal of Data Science· 0 citations
A structured multi-metric evaluation framework that integrates classification, ranking-based, calibration, and robustness-oriented metrics for comprehensive ML performance assessment is proposed capable of supporting more reliable real-world ML deployment.
Ade Putra, E. Noche, Diksha D. Gabhane· Journal of Data Science· 0 citations
An uncertainty-aware machine learning framework that integrates probabilistic modeling techniques into conventional predictive architectures to jointly estimate epistemic and aleatoric uncertainty is proposed, indicating that incorporating uncertainty estimation enhances trustworthiness and robustness without sacrifici...
Evi Yulianingsih, E. Noche, V. Yadav et al.· Journal of Data Science· 0 citations
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