ОСОБЛИВОСТІ ПРОГРАМНИХ РІШЕНЬМОДЕЛЕЙ ЛОГІЧНИХ ДЕРЕВ КЛАСИФІКАЦІЇ НА ОСНОВІ СЕЛЕКЦІЇ НАБОРІВ ЕЛЕМЕНТАРНИХ ОЗНАК

Abstract

Urgency of the research.Currently there are several independent approaches (concepts) to solve the classification problem in the general setting, and the development of various concepts, approaches, methods, and models that cover the general issues of the theory of artificial intelligence and information systems, all of these approaches in a recognition theory have their advantages and disadvantages and form a single tool to solve applied problems of the theory of artificial intelligence. This study will focus on the current concept of decision trees (classification trees). The general problem of software (algorithmic) construction of logical recognition trees (classification) is considered. The object of this research is logical classification trees (LСT structures). The subject of the research is actual methods and algorithmic schemes for constructing logical classification trees. Target setting.The main existing methods and algorithms for working with arrays of discrete information in the construc-tion of recognition functions (classifiers) do not allow you to achieve a predetermined level of accuracy (efficiency) of the classification system and regulate their complexity in the construction process. However, this disadvantage is absent in meth-ods and schemes for building recognition systems based on the concept of logical classification trees (decision trees). That is, the coverage of the training sample the set of elementary signs in the case of LCT generates a fixed tree data structure (model LCT), which provides compression and conversion initial data TS, and therefore allows significant optimization and savings of hardware resources of the system, and is based on a single methodology – the optimal approximation test sample set of elementary features (attributes) that are included in some schema (operator) constructed in the learning process.Actual scientific researches and issues analysis. The possibility of an effective and economical software (algorithmic) scheme for constructing a logical classification tree (LCT structuremodel) based on the source arrays of training samples (arrays of discrete information) of a large sample.The research objective. Development of a simple and high-quality software method (algorithm and software system) for building models (structures) LCTfor large arrays of initial samples by synthesizing minimal forms of classification and recog-nition trees that provide an effective approximation of educational information with a set of ranked elementary features (at-tributes) is created on the basis of ascheme for branched feature selection in a wide range of applied problems.The statement of basic materials. We propose a general program scheme for constructing structures of logical classifi-cation trees, which for a given initial training sample builds a tree structure (classification model), which consists of a set of elementary features evaluated at each step of building the model for this sample. A method and ready-made software system build logic trees the main idea is to approximate the initial random sampling of the volume set of elementary features. This method provides the selection of the most informative (qualitative) elementary features from the source set when forming the current vertex of the logical tree (node). This approach allows to 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.Conclusions. The developed and proposed mathematical support for constructing LCT structures (classification tree mod-els) allows it to be used for solving a wide range of practical problems of recognition and classification, and the prospectsfor further research may consist in creating a limited method of logical classification tree (LCT structures), which consists in maintaining the criterion for stopping the procedure for constructing a logical tree by the depth of the structure, optimizing its software implementations, as well as experimental studies of this method for a wider range of practicalproblems.