LOGICAL RECOGNITION TREE CONSTRUCTION ON THE BASIS OF A STEP-TO-STEP ELEMENTARY ATTRIBUTE SELECTION
DOI:
https://doi.org/10.15588/1607-3274-2020-2-10Keywords:
Logical classification tree, image recognition, classification, attribute, branching criterion.Abstract
Context. A general problem of constructing logical recognition/classification trees has been analyzed. Logical classification trees are the object of the present study. The subject of the study are the relevant methods and algorithms of logical classification trees.
Objective. The goal of this work is to develop a simple and efficient method of constructing logical tree-like models on the basis of classification trees for training discrete information selection characterized by a structure of constructed logical classification trees from elementary attributes estimated on the basis of their informativeness calculation functional.
Method. A general method of constructing logical classification trees is suggested that constructs a tree-like structure for a given initial training selection comprising a set of elementary attributes estimated at each step of constructing a model according to the above selection. In other words, a method of constructing logical classification trees is suggested with the main idea of approximating the initial selection of an arbitrary volume by the elementary attribute set. This method during the current logical tree (node) vertex formation provides selecting the most informative (high-quality) elementary attributes from the initial set. Such approach at constructing the resulting classification tree allows one to reduce essentially the tree size and complexity (i.e. the total number of branches and structural layers) and increase the quality of its further analysis (interpretability). The method of constructing logical classification trees suggested by us enables one to construct the tree-like models for a wide class of artificial intellect theory problems.
Results. The method developed and presented in this work has received a software realization and was studied when solving a problem of classifying the geological type data characterized by a large-dimension attribute space.
Conclusions. Experiments carried out in this work have confirmed the efficiency of the software suggested and demonstrate the possibility of its use for solving a wide spectrum of applied recognition and classification problems. The outlook of the further studies may be related to creating a limited method of logical classification tree by introducing the stopping criterion for the logical tree construction procedure according to the structure depth, its program realization optimization, as well as to the experimental study of this method in a wider circle of applied problems.
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