RDDA: Rectified Discriminatory Delta-Adjust

This paper introduces Rectified Discriminatory Delta-Adjust (RDDA), a novel methodology that enhances interpolation-based predictive modeling through adaptive sensitivity parameters. Building upon the foundational Delta-Adjust algorithm, RDDA addresses the limitations of fixed sensitivity parameters by incorporating three dynamic sensitivity estimation methodologies: Sensitivity Analysis (SA), Vector Calculus (VC), and Higher-Order Derivative Methods. The research establishes theoretical foundations for local-to-global emergence in inductive AI systems, proving that local inference mechanisms can reconstruct global information structures through the equivalence of local propagation and global entanglement views of shared information. We demonstrate that temporal ordering in datasets affects information flow profiles, with discriminatory coding revealing that data correlations are non-uniformly distributed across datasets. RDDA’s modular architecture allows plug-in sensitivity estimators to replace fixed parameters with query-adaptive, data-driven sensitivity metrics. Experimental validation across classification, regression, and interpolation tasks demonstrates the competitiveness of the RDDA framework. Its variants sometimes outperform the vanilla Delta-Adjust method. On interpolation benchmarks, RDDA matches the accuracy of dedicated methods like IDW and RBF, while on classification and regression tasks, it delivers performance comparable to established models including SVMs, KNNs, and Random Forests. The methodology preserves Delta-Adjust’s linear time core complexity while adding modular sensitivity estimation overhead, enabling practical deployment in data-driven modeling applications where local-to-global emergence is essential.