A medical model's benchmark score does not establish that the same conclusion holds under a different evaluation. This study tests whether claims about model ranking, score reliability and screening performance survive changes in cohort, prompt, negative spectrum, specified prevalence and operating threshold. We audit...
Random vector functional link (RVFL) networks have gained considerable attention as efficient randomized learning models due to their fast training and simple architecture. However, classical RVFL still suffers from three fundamental limitations: the reliance on the squared loss makes it highly vulnerable to noise and...
Kausar Ali, M. Akhtar, A. Zafar et al.· IEEE transactions on fuzzy s...· 0 citations
Bounded predictive influence and reliability-guided geometry as complementary mechanisms for imbalanced learning with uncertain labels are supported as complementary mechanisms for imbalanced learning with uncertain labels.
M. Akhtar, J. Akarsh, M. Tanveer et al.· 0 citations
Experiments on UCI benchmark datasets validate the superiority of the proposed IFW-BLS model over the baseline models over the baseline models; additional corruption experiments also show more stable performance than BLS under noise and outlier contamination.
The kernel risk-sensitive mean p-power based RVFL (KRPRVFL) model is proposed, which integrates the computational efficiency of RVFL with the robustness of the kernel risk-sensitive mean p-power (KRP) criterion and adaptively reduces the influence of corrupted or unreliable samples during training, resulting in improve...
A. Quadir, A. Rahaman, M. Akhtar et al.· 0 citations
Wave-BLS, a robust broad learning framework that integrates the wave loss function, which is asymmetric, bounded, and smooth, enabling controlled penalization of large errors, is proposed, establishing Wave-BLS as a stable and robust alternative to existing broad learning models for learning under data uncertainty.
M. Akhtar, A. Varshney, A. Quadir et al.· 0 citations
The first complex augmented Broad Learning System (CA-BLS) is introduced, which transforms real-valued inputs into phase-encoded complex representations and adopts widely linear modeling to jointly leverage covariance and pseudo-covariance information via complex conjugate augmentation, enabling effective modeling of l...
A. Rahaman, A. Quadir, M. Sajid et al.· 0 citations
A simple and broadly applicable residual guided procedure that greedily constructs the hidden layer using a closed form residual decrease criterion and yields a progressive training process with a guaranteed monotonic decrease of the training objective.
M. Akhtar, M. Tanveer, Mohd. Arshad· 0 citations
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