Method of algorithmic classification trees based on constraints

Abstract
The general problem of constructing algorithmic recognition (classification) trees based on a limited method in the theory of artificial intelligence is considered. The object of this research is the concept of an algorithmic classification tree based on a bounded method. The subject of the research is actual methods, algorithms and schemes (limited method) for constructing algorithmic classification trees. A limited method for constructing algorithmic classification trees is proposed, which for a given initial training sample of any size builds a tree structure (algorithm tree model), which consists of a set of autonomous classification algorithms and recognition evaluated at each stage of constructing algorithm trees based on this initial sample. That is, a limited method for constructing an algorithmic classification tree is proposed, the main idea of which is a step-by-step approximation of the initial sample of an arbitrary volume and structure by a set of independent classification and recognition algorithms. This method, when forming the current vertex of the algorithmic tree, ensures that the most efficient (high-quality) autonomous classification algorithms are selected from the initial set and only those paths in the tree structure where the greatest number of classification errors occur are completed. The limited method of constructing an algorithmic classification tree makes it possible to build different types of tree-like recognition models with predefined accuracy for a wide class of problems in the theory of artificial intelligence. The limited method of the algorithmic classification tree developed and presented in this paper received a software implementation and was investigated and compared with the methods of logical classification trees, methods of the algorithmic classification tree (first and second types) when solving the problem of recognizing real geological data. Figs.: 2. Tabl.: 1. Refs.: 34 titles.