IFW-BLS is a Dual‑Robust Broad Learning System that enhances the standard Broad Learning System by incorporating two robustness mechanisms. It replaces the squared loss with a bounded, smooth, asymmetric wave loss to protect against large residuals, and applies intuitionistic fuzzy scores to weight samples based on global class consistency and local neighborhood conflict, thereby down‑weighting unreliable data. The model is optimized with a Nesterov accelerated gradient solver, avoiding explicit matrix inversion, and experiments on UCI benchmarks show it outperforms baseline models and remains stable under noise and outlier contamination.
By Mushir Akhtar, M. Tanveer
The paper introduces IFW-BLS, a Dual‑Robust Broad Learning System that enhances the traditional BLS by incorporating a bounded, asymmetric wave loss to protect against large residuals and by applying intuitionistic fuzzy scores to weight samples based on credibility. This dual approach mitigates the impact of noise, outliers, and ambiguous data points, while a Nesterov‑accelerated optimizer replaces the costly matrix inversion of conventional BLS. Experiments on UCI benchmarks and corruption tests demonstrate that IFW‑BLS outperforms baseline models and remains more stable under noisy conditions.
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