Mathematics and Computational Sciences

Intuitionistic complex Fuzzy sets in decision support systems: A choquet operated data mining-ANN approach

Document Type : Original Article

Authors

Chandrasekar Karpaha 1
John Robinson P 1
Sunday Emmanuel Fadugba 2

1 Department of Mathematics, Bishop Heber College, Affiliated to Bharathidasan University, Tiruchirappalli, India.

2 Department of Mathematics, Ekiti State University, Ado Ekiti, Nigeria.

https://doi.org/10.30511/mcs.2025.2075301.1533
Abstract
This work introduces a novel set, the Intuitionistic Complex Fuzzy Set (ICFS), that expands traditional intuitionistic fuzzy sets into a complex-valued domain and captures interacting attributes more effectively in the decision support framework based on ICFS. A new aggregation operator called the Intuitionistic Complex Fuzzy Einstein Correlated Geometric (ICFECG) operator and a new score and accuracy function for the ICFS are proposed and to ensure theoretical robustness, rigorous proofs are provided for multiple theorems associated with the newly developed ICFECG operator, the score and the accuracy functions. This operator effectively combines expert opinions while preserving both the amplitude and phase components of complex uncertainty, thereby ensuring that the aggregated information accurately reflects the full structure of the intuitionistic complex fuzzy evaluations. To improve efficiency in solving MAGDM problems, a data mining–based dimensionality reduction strategy that helps identify and remove redundant or weakly influential attributes is introduced. Artificial Neural Network (ANN) techniques are also incorporated to enhance the learning ability and optimization of the decision-support process. A new defuzzification function is proposed to integrate all the ICFS components, yielding a crisp value for enhancing the data mining and ANN computations. The final hybrid model combines ICFS theory, the ICFECG operator, data mining, and ANN optimization which effectively handles high-dimensional, correlated, and uncertain information arising in the decision making environment. A numerical case study shows that our methodology reduces the computational load, removes insignificant alternatives, and significantly improves decision accuracy, stability, and reliability.

Keywords

Intuitionistic Complex Fuzzy set
MAGDM
Choquet integral operator
Data Mining
ANN

Subjects

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Mathematics and Computational Sciences
Volume 7, Issue 1
Winter 2026
Pages 65-83

  • Receive Date 20 October 2025
  • Revise Date 28 November 2025
  • Accept Date 29 November 2025

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