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Adaptive Neuro-Fuzzy Inference System Optimized by Cuckoo Search Algorithm for Multi-Domain Regression and Classification Problems.

Nader Behdad
Volume 5

Abstract

Multi-domain regression and classification problems are characterized by heterogeneous data structures, nonlinear input-output relationships, uncertainty, noise, missing values, and domain-specific feature interactions, making it difficult for conventional statistical and machine learning models to achieve robust and generalizable performance across different application areas. Adaptive Neuro-Fuzzy Inference Systems offer a powerful hybrid modeling approach by combining the learning capability of neural networks with the linguistic interpretability of fuzzy logic; however, their effectiveness is highly dependent on the appropriate selection of membership functions, fuzzy rules, premise parameters, and consequent parameters. This paper proposes an Adaptive Neuro-Fuzzy Inference System optimized by the Cuckoo Search Algorithm for multi-domain regression and classification problems. The proposed framework employs the global search capability of Cuckoo Search to automatically optimize ANFIS structure and parameters, including membership function type, number of fuzzy rules, rule weights, learning coefficients, and parameter initialization, thereby reducing the limitations of manual configuration and improving convergence toward high-quality solutions. The framework was evaluated across diverse domains, including medical diagnosis, energy forecasting, environmental monitoring, financial risk prediction, industrial fault detection, and benchmark regression datasets. A comprehensive preprocessing pipeline was applied, including missing value imputation, outlier treatment, normalization, feature selection, categorical encoding, class imbalance handling, and train-validation-test splitting to ensure reliable model assessment. For regression tasks, the optimized ANFIS model was assessed using RMSE, MAE, MAPE, and coefficient of determination, while classification tasks were evaluated using accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC-ROC. Experimental results demonstrate that the proposed CS-ANFIS framework achieves superior predictive performance compared with conventional ANFIS, standalone fuzzy inference systems, artificial neural networks, Support Vector Machine, Random Forest, Gradient Boosting, Genetic Algorithm-optimized ANFIS, and Particle Swarm Optimization-based ANFIS baselines. The optimized framework records improved prediction accuracy, lower error rates, faster convergence, and stronger generalization across heterogeneous datasets. Furthermore, the fuzzy rule base generated by the proposed model provides interpretable decision patterns that help explain the relationship between input features and predicted outcomes, supporting transparency in high-impact domains. The findings indicate that Cuckoo Search-optimized ANFIS provides an adaptive, interpretable, and computationally efficient solution for multi-domain regression and classification, offering practical value for intelligent decision-support systems that require both predictive accuracy and human-understandable reasoning.

Keywords

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