TY  - JOUR
AU  - Khan, Mufala
AU  - Kumar, Rakesh
AU  - Dhiman, Gaurav
PY  - 2025
DA  - 2025/06/02
TI  - A Hybrid Machine Learning Fuzzy Non-linear Regression Approach for Neutrosophic Fuzzy Set
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 1
IS  - 1
SP  - 42
EP  - 51
DO  - 10.62762/TMI.2025.561363
UR  - https://www.icck.org/article/abs/TMI.2025.561363
KW  - fuzzy sets
KW  - regression analysis
KW  - fuzzy non-linear regression model
KW  - neutrosophic fuzzy set
AB  - Neutrosophic sets play a significant role for handling indeterminacy. In this paper, we introduce a novel hybrid fuzzy non-linear regression model that combines a neural-network-based initialisation stage with a Kuhn-Tucker constrained optimisation stage 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 the KKT-based model. The hybrid approach reduces the maximum spread by 22.09% and improves prediction accuracy, as shown by a 21.90% 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 and offers a powerful tool for improved uncertainty quantification in nonlinear systems.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Khan2025A,
  author = {Mufala Khan and Rakesh Kumar and Gaurav Dhiman},
  title = {A Hybrid Machine Learning Fuzzy Non-linear Regression Approach for Neutrosophic Fuzzy Set},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {42-51},
  doi = {10.62762/TMI.2025.561363},
  url = {https://www.icck.org/article/abs/TMI.2025.561363},
  abstract = {Neutrosophic sets play a significant role for handling indeterminacy. In this paper, we introduce a novel hybrid fuzzy non-linear regression model that combines a neural-network-based initialisation stage with a Kuhn-Tucker constrained optimisation stage 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 the KKT-based model. The hybrid approach reduces the maximum spread by 22.09\% and improves prediction accuracy, as shown by a 21.90\% 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 and offers a powerful tool for improved uncertainty quantification in nonlinear systems.},
  keywords = {fuzzy sets, regression analysis, fuzzy non-linear regression model, neutrosophic fuzzy set},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}