Optimal spanning tree reconstruction in symbolic regression

• URL: http://arxiv.org/abs/2406.18612v1
• Date: Tue, 25 Jun 2024 13:22:13 GMT
• Title: Optimal spanning tree reconstruction in symbolic regression
• Authors: Radoslav G. Neychev, Innokentiy A. Shibaev, Vadim V. Strijov,
• Abstract summary: A model is a superposition of primitive functions. The proposed algorithm reconstructs theminimum spanning tree from theweighted colored graph. This paper presents a novel solution based on the prize-collecting Steiner tree algorithm.
• Score: 2.553456266022125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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