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The paper proposes intuitionistic fuzzy deep RVFL (IF-dRVFL) and ensemble deep RVFL (IF-edRVFL) frameworks that use sample neighborhood information to improve robustness against noise and outliers in classification tasks, outperforming existing SOTA fuzzy and non-fuzzy approaches on benchmark datasets.
Introduces CAWI, a copula-based weight initialization method for randomized neural networks that models inter-feature dependence, improving predictive performance across 83 classification benchmarks.