Genetic Programming for Explainable Manifold Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8389

@Article{Cravens:TETCI,

• author = "Ben Cravens and Andrew Lensen and Paula Maddigan and Bing Xue",
• title = "Genetic Programming for Explainable Manifold Learning",
• journal = "IEEE Transactions on Emerging Topics in Computational Intelligence",
• keywords = "genetic algorithms, genetic programming, Complexity theory, Manifold learning, Measurement, Optimisation, Computational modelling, Feature extraction, High dimensional data, Evolutionary computation, Syntactics, dimensionality reduction, explainable artificial intelligence",
• ISSN = "2471-285X",
• DOI = "doi:10.1109/TETCI.2025.3561666",
• abstract = "Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thereby enhancing the efficiency, interpretability, and scalability of data analysis. Despite their utility, current manifold learning methods often lack explicit functional mappings, which are critical for ensuring explainability in regulated and high-stakes applications. This paper introduces Genetic Programming for Explainable Manifold Learning (GP-EMaL), a novel integration of Genetic Programming (GP) and Explainable Artificial Intelligence (XAI). GP-EMaL leverages the inherently interpretable, tree-based structures of GP to generate explicit, functional mappings while directly addressing complexity challenges through innovative penalties for tree size, symmetry, and operator selection. By enabling customisable complexity metrics, GP-EMaL adapts to diverse application needs, achieving high manifold quality and significantly improved explainability. Comprehensive experiments demonstrate that GP-EMaL matches or exceeds the performance of existing approaches, producing simpler and more interpretable models. This work advances the state of explainable manifold learning, paving the way for its adoption in domains such as healthcare, environmental modelling, and financial analysis.",
• notes = "Also known as \cite{10979286} see \cite{DBLP:journals/corr/abs-2403-14139,}",

}

Genetic Programming entries for Ben Cravens Andrew Lensen Paula Maddigan Bing Xue

Citations