: 25-32
Received: October 15, 2022
Accepted: October 17, 2022

Ци­ту­ван­ня за ДСТУ: Пов­хан І. Ф. Ме­тод син­те­зу ло­гіч­них де­рев кла­си­фі­ка­ції на під­ста­ві се­лек­ції еле­мен­тар­них оз­нак. Ук­ра­їнсь­кий жур­нал ін­фор­ма­ційних тех­но­ло­гій. 2022, т. 4, № 2. С. 25–32.

Ci­ta­ti­on APA: Povkhan, I. F. (2022). Met­hod for synthe­si­zing lo­gi­cal clas­si­fi­ca­ti­on tre­es ba­sed on the se­lec­ti­on of ele­men­tary fe­atu­res. Uk­ra­ini­an Jo­ur­nal of In­for­ma­ti­on Techno­logy, 4(2), 25–32.

Uzhhorod National University, Uzhhorod, Ukraine

The general problem of constructing logical recognition and classification trees is considered. The object of this study is logical classification trees. The subject of the research is current methods and algorithms for constructing logical classification trees. The aim of the work is to create a simple and effective method for constructing recognition models based on classification trees for training samples of discrete information, which is characterized by elementary features in the structure of synthesized logical classification trees. A general method for constructing logical classification trees is proposed, which builds a tree structure for a given initial training sample, which consists of a set of elementary features evaluated at each step of building a model for this sample. A method for constructing a logical tree is proposed, the main idea of which is to approximate the initial sample of an arbitrary volume with a set of elementary features. When forming the current vertex of the logical tree, the node provides selection of the most informative, qualitative elementary features from the original set. This approach, when constructing the resulting classification tree, can significantly reduce the size and complexity of the tree, the total number of branches and tiers of the structure, and improve the quality of its subsequent analysis. The proposed method for constructing a logical classification tree makes it possible to build tree-like recognition models for a wide class of problems in the theory of artificial intelligence. The method developed and presented in this paper received a software implementation and was investigated when solving the problem of classifying geological data. The experiments carried out in this paper confirmed the operability of the proposed mathematical support and show the possibility of using it to solve a wide range of practical recognition and classification problems. Prospects for further research may consist in creating a limited method of the logical classification tree, which consists in maintaining a criterion for stopping the procedure for constructing a logical tree according to the depth of the structure, optimizing its software implementations, as well as experimental studies of this method for a wider range of practical tasks.

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