A Hybrid Machine Learning Fuzzy Non-linear Regression Approach for Neutrosophic Fuzzy Set
Mufala Khan, Rakesh Kumar, Gaurav Dhiman
Abstract
Neutrosophic sets play a significant role for handling indeterminacy. In this paper, we introduce a novel fuzzy non-linear regression model to find the minimum spread of neutrosophic fuzzy sets. Kuhn-Tucker's necessary conditions are employed to estimate the parameters for non-linear regression models, which can be applied to any data set. The resulting hybrid model possesses the ability to minimise the spread of uncertainty in a much better fashion than the existing non-linear regression contenders which rely on KKT- based model. The hybrid approach reduces the maximum spread by 22.09% and improves prediction accuracy, as shown by a 22.23% reduction in RMSE. The study’s findings highlight the hybrid model’s ability to achieve tighter spreads and enhanced predictive reliability, particularly in complex systems where uncertainties in data are significant. This research contributes to advancing fuzzy regression techniques, offering a powerful tool for improved uncertainty quantification in nonlinear systems.