The problem of convergence of the procedure for synthesizing classifier schemes in the methods of logical and algorithmic classification trees is considered. An upper estimate of the complexity of the algorithm tree scheme is proposed in the problem of approximating an array of real data with a set of generalized features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree. This approach allows you to ensure the necessary accuracy of the model, assess its complexity, reduce the number of branches and achieve the necessary performance indicators. For the first time, methods for constructing structures of logical and algorithmic classification trees are given an upper estimate of the convergence of constructing classification trees. The proposed convergence estimate of the procedure for constructing classifiers for LCT/ACT structures makes it possible to build economical and efficient classification models of a given accuracy. The method of constructing an algorithmic classification tree is based on a step-by-step approximation of an initial sample of arbitrary volume and structure by a set of independent classification algorithms. When forming the current vertex of an algorithmic tree, node, or generalized feature, this method highlights the most efficient, high-quality autonomous classification algorithms from the initial set. This approach to constructing the resulting classification tree can significantly reduce the size and complexity of the tree, the total number of branches, vertices, and tiers of the structure, improve the quality of its subsequent analysis, interpretability, and ability to decompose. Methods for synthesizing logical and algorithmic classification trees were implemented in the library of algorithms of the “Orion III” software system for solving various applied problems of artificial intelligence. Practical applications have confirmed the operability of the constructed classification tree models and the developed software. The paper estimates the convergence of the procedure for constructing recognition schemes for cases of logical and algorithmic classification trees under conditions of weak and strong class separation of the initial sample. Prospects for further research and testing may consist in evaluating the convergence of the ACT synthesis procedure in a limited method of the algorithmic classification tree, which consists in maintaining a criterion for stopping the procedure for constructing a tree model by the depth of the structure, optimizing its software implementations, introducing new types of algorithmic trees, as well as experimental studies of this method for a wider range of practical problems.
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