Using Genetic Programming to Find Functional Mappings for UMAP Embeddings

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Schofield:2021:CEC,

• author = "Finn Schofield and Andrew Lensen",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Using Genetic Programming to Find Functional Mappings for {UMAP} Embeddings",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "704--711",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• keywords = "genetic algorithms, genetic programming, Manifolds, Visualization, Dermatology, Evolutionary computation, Cost function, Manifold learning, Manifold learning, Dimensionality Reduction, Feature Construction, Feature Selection",
• isbn13 = "978-1-7281-8393-0",
• URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/29139072",
• DOI = "doi:10.1109/CEC45853.2021.9504848",
• size = "8 pages",
• abstract = "Manifold learning is a widely used technique for reducing the dimensionality of complex data to make it more understandable and more efficient to work with. However, current state-of-the-art manifold learning techniques, such as Uniform Manifold Approximation and Projection (UMAP), have a critical limitation. They do not provide a functional mapping from the higher dimensional space to the lower-dimensional space, instead, they produce only the lower-dimensional embedding. This means they are {"}black-boxes{"} that cannot be used in domains where explainability is paramount. Recently, there has been work on using genetic programming to perform manifold learning with functional mappings (represented by tree/s), however, these methods are limited in their performance compared to UMAP. To address this, in this work we propose using UMAP to create functional mappings with genetic programming-based manifold learning. We compare two different approaches: one that uses the embedding produced by UMAP as the target for the functional mapping; and the other which directly optimises the UMAP cost function by using it as the fitness function. Experimental results reinforce the value of producing a functional mapping and show promising performance compared to UMAP. Additionally, we visualise two-dimensional embeddings produced by our technique compared to UMAP to further analyse the behaviour of each of the algorithms.",
• notes = "Also known as \cite{9504848}",

}

Genetic Programming entries for Finn Schofield Andrew Lensen

Citations