Yedilkhan D., Zhalgasbayev A., Saleshova S., Khaimuldin N. OIKAN: A Hybrid AI Framework Combining Symbolic Inference and Deep Learning for Interpretable Information Retrieval Models // Algorithms. – 2025. – Vol. 18. – Art. 639. – DOI: 10.3390/a18100639.

Abstract:
The rapid expansion of AI applications in various domains demands models that balance predictive power with human interpretability, a requirement that has catalyzed the development of hybrid algorithms combining high accuracy with human-readable outputs. This study introduces a novel neuro-symbolic framework, OIKAN (Optimized Interpretable Kolmogorov–Arnold Network), designed to integrate the representational power of feedforward neural networks with the transparency of symbolic regression. The framework employs Gaussian noise-based data augmentation and a two-phase sparse symbolic regression pipeline using ElasticNet, producing analytical expressions suitable for both classification and regression problems. Evaluated on 60 classification and 58 regression datasets from the Penn Machine Learning Benchmarks (PMLB), OIKAN Classifier achieved a median accuracy of 0.886, with perfect performance on linearly separable datasets, while OIKAN Regressor reached a median R² score of 0.705, peaking at 0.992. In comparative experiments with ElasticNet, DecisionTree, and XGBoost baselines, OIKAN showed competitive accuracy while maintaining substantially higher interpretability, highlighting its distinct contribution to the field of explainable AI. OIKAN demonstrated computational efficiency, with fast training and low inference time and memory usage, highlighting its suitability for real-time and embedded applications. However, the results revealed that performance declined more noticeably on high-dimensional or noisy datasets, particularly those lacking compact symbolic structures, emphasizing the need for adaptive regularization, expanded function libraries, and refined augmentation strategies to enhance robustness and scalability. These results underscore OIKAN’s ability to deliver transparent, mathematically tractable models without sacrificing performance, paving the way for explainable AI in scientific discovery and industrial engineering.

Link / DOI: https://doi.org/10.3390/a18100639