Volume 6 • Issue 1 • PP: 26–33 • 2026
Dual-Indeterminacy Neutrosophic Fusion for Calibrated and Selective Multi-Source Classification under Source Conflict
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© 2026 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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Reliable multi-source AI requires a distinction between two non-equivalent failure modes: within-source ambiguity and between-source contradiction. This paper formulates dual-indeterminacy neutrosophic fusion (DINF), in which calibrated posteriors ps ∈ ΔK−1 are transformed into class-wise triples Nk = ⟨Tk, Ik,Fk⟩. Source weights combine chance-corrected validation reliability, normalized entropy, and a robust Jensen–Shannon consensus discount. Intrinsic ambiguity U and class-specific contradiction Ck are retained separately and joined by Ik =U +Ck −UCk. The decision distribution qk ∝ Tk exp(−λIk) therefore penalizes conflict without collapsing neutrosophic evidence into a probability simplex. Boundedness, permutation invariance, consensus preservation, conflict monotonicity, log-odds sensitivity, and missing-source neutrality are established. Repeated experiments on three public benchmarks examine clean data, 30% source dropout, 30% confident contradiction, and 40% mixed failure. Under contradiction, DINF achieves macro-F1 0.910, NLL 0.445, and selective accuracy 0.939 at approximately 90% coverage; the corresponding logarithmic-pool values are 0.896, 0.469, and 0.929. The results identify robust agreement discounting and explicit contradiction-sensitive indeterminacy as complementary mechanisms for calibrated failure-aware fusion.
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