Optimal spanning tree reconstruction in symbolic regression

Fuente: arXiv
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Auteurs principaux: Neychev, Radoslav G., Shibaev, Innokentiy A., Strijov, Vadim V.
Format: Preprint
Publié: 2024
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author Neychev, Radoslav G.
Shibaev, Innokentiy A.
Strijov, Vadim V.
author_facet Neychev, Radoslav G.
Shibaev, Innokentiy A.
Strijov, Vadim V.
contents This paper investigates the problem of regression model generation. A model is a superposition of primitive functions. The model structure is described by a weighted colored graph. Each graph vertex corresponds to some primitive function. An edge assigns a superposition of two functions. The weight of an edge equals the probability of superposition. To generate an optimal model one has to reconstruct its structure from its graph adjacency matrix. The proposed algorithm reconstructs the~minimum spanning tree from the~weighted colored graph. This paper presents a novel solution based on the prize-collecting Steiner tree algorithm. This algorithm is compared with its alternatives.
format Preprint
id arxiv_https___arxiv_org_abs_2406_18612
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Optimal spanning tree reconstruction in symbolic regression
Neychev, Radoslav G.
Shibaev, Innokentiy A.
Strijov, Vadim V.
Machine Learning
This paper investigates the problem of regression model generation. A model is a superposition of primitive functions. The model structure is described by a weighted colored graph. Each graph vertex corresponds to some primitive function. An edge assigns a superposition of two functions. The weight of an edge equals the probability of superposition. To generate an optimal model one has to reconstruct its structure from its graph adjacency matrix. The proposed algorithm reconstructs the~minimum spanning tree from the~weighted colored graph. This paper presents a novel solution based on the prize-collecting Steiner tree algorithm. This algorithm is compared with its alternatives.
title Optimal spanning tree reconstruction in symbolic regression
topic Machine Learning
url https://arxiv.org/abs/2406.18612