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.

翻译：本文研究了回归模型生成问题。模型是原始函数的叠加。模型结构由加权着色图描述。每个图顶点对应某个原始函数。边表示两个函数的叠加。边的权重等于叠加的概率。为生成最优模型，需从图的邻接矩阵重构其结构。所提算法从加权着色图中重构最小生成树。本文提出了一种基于带奖励的Steiner树算法的新颖解决方案，并与替代算法进行了比较。