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Author

Asif Ahmed

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Book Open access Aug 2026

NiWo: An Augmentation Framework to Enhance ML Performance and Interpretability for Tabular Data with Class Imbalance

NiWo optimizes the weights of influential neighborhood instances within an augmentation budget, thus preserving computational efficiency and offering interpretability, and outperforms other augmentation methods at enhancing ML performance, especially over datasets with class imbalance and scarce instances.

Asif Ahmed, Sakhawat Hossain Saimon, Jianhua Ruan et al. · 0 citations
Book Open access Aug 2026

NiWo: An Augmentation Framework to Enhance ML Performance and Interpretability for Tabular Data with Class Imbalance

Various data augmentation methods have been proposed to address class imbalance in Machine Learning (ML) and Artificial Intelligence tasks across multiple data modalities. For tabular data, augmentation methods must be interpretable so that human decision-makers can audit the process (e.g., which neighborhoods are bein...

Asif Ahmed, Sakhawat Hossain Saimon, Jianhua Ruan et al. · 0 citations

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