Context. The general problem of constructing logical trees of recognition (classification) in the theory of artificial intelligence is considered in this paper. The object of this study is the concept of the classification tree (a logical and an algorithmic ones). The current methods and algorithms for constructing algorithmic classification trees are the subject of the study. Objective. This work aims to create a simple and effective method for constructing tree-like recognition models on the basis of algorithmic classification trees for the training set of discrete information, which is characterized by the structure of the logical classification trees obtained on the basis of independent classification algorithms evaluated through the functional of calculating their overall efficiency. Method. The general method of constructing algorithmic classification trees is proposed. It builds a tree-like structure (a classification model) for a given initial training data set. This structure consists of a set of autonomous algorithms of classification and recognition which have been evaluated at each step (stage) of constructing the model based on the given initial dataset. Namely, the method for constructing the algorithmic classification tree is proposed. The main idea of this method is to step by step approximate the initial dataset of arbitrary size and structure using a set of independent classification algorithms. This method, when forming the current vertex of the algorithmic tree (a node, a generalized feature) ensures the selection of the most effective (highquality) autonomous classification algorithms from the initial dataset. In the process of constructing the resulting classification tree this approach can significantly reduce the size and complexity of the tree (the total number of branches, vertices and tiers of the structure) and improve the quality of its subsequent analysis (interpretability), the possibility of decomposition. The proposed method of constructing an algorithmic classification tree enables building different types of tree-like recognition models for a wide class of problems in the theory of artificial intelligence. Results. The algorithmic classification tree method, developed and presented in this work, was implemented in the software and was studied and compared with the methods of logical classification trees (based on the selection of a set of elementary features) when solving the problem of recognizing real data of the geologic type. Conclusions. The results of the conducted experiments described in this paper confirm the functional efficiency of the proposed mathematical software and show the possibility of its future use for solving a wide range of practical problems of recognition and classification. Further research prospects and approbation may consist in developing a limited method of the algorithmic classification tree, whose main points include the introduction of the criterion for stopping the procedure of constructing a tree model based on the depth of the structure, optim...
The article describes modern technologies for organizing online learning. The article provides a brief analysis of some of the modern plan forms for organizing online learning.Аннотация В статье описываются современные технологии для организации онлайнөобучения. В статье дается краткий анализ некоторых из современных планформ для организации онлайн-обучения.
This paper reports the analysis of a forecasting problem based on time series. It is noted that the forecasting stage itself is preceded by the stages of selection of forecasting methods, determining the criterion for the forecast quality, and setting the optimal prehistory step. As one of the criteria for a forecast quality, its volatility has been considered. Improving the volatility of the forecast could ensure a decrease in the absolute value of the deviation of forecast values from actual data. Such a criterion should be used in forecasting in medicine and other socially important sectors. To implement the principle of competition between forecasting methods, it is proposed to categorize them based on the values of deviations in the forecast results from the exact values of the elements of the time series. The concept of dominance among forecasting methods has been introduced; rules for the selection of dominant and accurate enough predictive models have been defined. Applying the devised rules could make it possible, at the preceding stages of the analysis of the time series, to reject in advance the models that would surely fail from the list of predictive models available to use. In accordance with the devised method, after applying those rules, a system of functions is built. The functions differ in the sets of predictive models whose forecasting results are taken into consideration. Variables in the functions are the weight coefficients with which predictive models are included. Optimal values for the variables, as well as the optimal model, are selected as a result of minimizing the functions built. The devised method was experimentally verified. It has been shown that the constructed method made it possible to reduce the forecast error from 0.477 and 0.427 for basic models to 0.395 and to improve the volatility of the forecast from 1969.489 and 1974.002 to 1607.065
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