Optimal spanning tree reconstruction in symbolic regression
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 11 references that are in the map. First posted on arXiv 25 Jun 2024.
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References · 1
- 2016Approximate Euclidean Steiner Treescited by 1
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