Volume 10 • Issue 2 • PP: 01 –09 • 2025
Neutrosophic–Fuzzy Multi-View Imputation for Incomplete Multivariate Measurement Data
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Three reconstructions of the same missing cell can each be defensible and still disagree materially. Let e=(eN,eS,eR) denote contextual-neighborhood, low-rank, and cross-variable ridge estimates. The central question is therefore not only which value should replace xi j, but how much coherent evidence supports that replacement. Neutrosophic–Fuzzy Multi-View Imputation (NFMVI) addresses this question by assigning each source a fuzzy reliability μs ∈ (0,1] and a Gaussian concordance as = exp[−(es−c)2/(2γ2)] around a reliability-weighted center c. These quantities generate the source state (Ts, Is,Fs) = (μsas,1−as,1− μs), from which ws = Ts/Σr Tr yields the convex reconstruction bx = Σswses. The same state produces a cell-level unresolved-evidence fraction U = 1−Σs Ts/Σs(Ts+Is+Fs), so reconstruction and uncertainty are generated by one mechanism rather than by separate post-processing. Evaluation uses two real multivariate datasets, MCAR, value-dependent MAR, and structured channel deletion at 10–30%, giving 144 common deterministic masks. The evidence is deliberately mixed. On the macroeconomic panel, NFMVI attains standardized RMSE 0.3425, below KNN (0.3776), Bayesian-ridge chained imputation (0.3794), and iterative SVD (0.4987); on the diabetes covariates, NFMVI (0.7641) and chained imputation (0.7659) are nearly indistinguishable overall, with chained imputation remaining better under MAR. The uncertainty signal is more distinctive: macro error-screening AUC averages 0.8156, and RMSE rises from 0.1717 in the lowest-U quartile to 0.6202 in the highest. Paired bootstrap contrasts, correlation-structure error, ablation, and parameter sensitivity therefore support a narrower conclusion than universal accuracy dominance: NFMVI provides an interpretable fuzzy–neutrosophic rule for reconciling heterogeneous imputers while retaining cell-level evidence conflict for downstream scrutiny.
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