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From Black Box to Symbolic Insight: Interpretable Machine Learning With T-Spline Networks
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Author(s): Marco Antonio Marquez-Vera (Polytechnic University of Pachuca, Mexico), Alfian Ma'arif (Universitas Ahmad Dahlan, Indonesia)and Blanca Diana Balderrama-Hernández (Secretariat of Public Education, Mexico)
Copyright: 2027
Pages: 30
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/407567
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Abstract
This article explores the use of Kolmogorov-Arnold Networks as interpretable machine learning models instead of the universal approach theorem used in machine and deep learning. Emphasis is placed on architectures based on B-splines, T-splines, and FastKAN using RBFs, which allow for transparent function approximation. The article discusses how symbolic representations emerge from trained models, the role of node pruning in simplifying structure, and the potential of these techniques to uncover latent physical models or aid in scientific modeling where interpretability is essential. Also, by pruning the neural model, it is possible to simplify the interpretable model.
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