Classification models of flood-related events based on algorithm trees

Povkhan, Igor

doi:10.15587/1729-4061.2020.219525

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Так з виразу [2] відомо, що результуюче правило класифікації (схема), яке побудоване довільним методом або алгоритмом розгалуженого вибору ознак, має деревоподібну логічну структуру. Логічне дерево складається з вершин (ознак), які групуються по ярусам і які отримані на певному кроці (етапі) побудови дерева розпізнавання [11], [18], [21]. Важливою задачею, яка виникає з [4], [7], [8], [10] задача синтезу дерев розпізнавання, які будуть представлятися фактично деревом (графом) алгоритмів.…”

Section: ʑ˔˕˖˒unclassified

The Method of Bounded Constructions of Logical Classification Trees in the Problem of Discrete Objects Classification

Povkhan¹

2021

UJIT

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The problem of constructing a model of logical classification trees based on a limited method of selecting elementary features for geological data arrays is considered. A method for approximating an array of real data with a set of elementary features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree is proposed. This approach allows to ensure the necessary accuracy of the model, reduce its structural complexity, and achieve the necessary performance indicators. A limited method for constructing classification trees has been developed, which is aimed at completing only those paths (tiers) of the classification tree structure where there are the greatest number of errors (of all types) of classification. This approach to synthesizing the recognition model makes it possible to effectively regulate the complexity (accuracy) of the classification tree model that is being built, and it is advisable to use it in situations with restrictions on the hardware resources of the information system, restrictions on the accuracy and structural complexity of the model, restrictions on the structure, sequence and depth of recognition of the training sample data array. The limited scheme of synthesis of classification trees allows to build models almost 20 % faster. The constructed logical classification tree will accurately classify (recognize) the entire training sample that the model is based on, will have a minimal structure (structural complexity), and will consist of components – sets of elementary features as design vertices, tree attributes. Based on the proposed modification of the elementary feature selection method, software has been developed that allows working with a set of different types of applied problems. An approach to synthesizing new recognition models based on a limited logic tree scheme and selecting pre-pruning parameters is proposed. In other words, an effective scheme for recognizing discrete objects has been developed based on step-by-step evaluation and selection of sets of attributes (generalized features) based on selected paths in the classification tree structure at each stage of scheme synthesis.

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Section: ʑ˔˕˖˒unclassified

The Method of Bounded Constructions of Logical Classification Trees in the Problem of Discrete Objects Classification

Povkhan¹

2021

UJIT

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Convergence Problem Schemes for Constructing Structures of Logical and Algorithmic Classification Trees

Povkhan¹

2022

UJIT

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The problem of convergence of the procedure for synthesizing classifier schemes in the methods of logical and algorithmic classification trees is considered. An upper estimate of the complexity of the algorithm tree scheme is proposed in the problem of approximating an array of real data with a set of generalized features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree. This approach allows you to ensure the necessary accuracy of the model, assess its complexity, reduce the number of branches and achieve the necessary performance indicators. For the first time, methods for constructing structures of logical and algorithmic classification trees are given an upper estimate of the convergence of constructing classification trees. The proposed convergence estimate of the procedure for constructing classifiers for LCT/ACT structures makes it possible to build economical and efficient classification models of a given accuracy. The method of constructing an algorithmic classification tree is based on a step-by-step approximation of an initial sample of arbitrary volume and structure by a set of independent classification algorithms. When forming the current vertex of an algorithmic tree, node, or generalized feature, this method highlights the most efficient, high-quality autonomous classification algorithms from the initial set. This approach to constructing the resulting classification tree can significantly reduce the size and complexity of the tree, the total number of branches, vertices, and tiers of the structure, improve the quality of its subsequent analysis, interpretability, and ability to decompose. Methods for synthesizing logical and algorithmic classification trees were implemented in the library of algorithms of the “Orion III” software system for solving various applied problems of artificial intelligence. Practical applications have confirmed the operability of the constructed classification tree models and the developed software. The paper estimates the convergence of the procedure for constructing recognition schemes for cases of logical and algorithmic classification trees under conditions of weak and strong class separation of the initial sample. Prospects for further research and testing may consist in evaluating the convergence of the ACT synthesis procedure in a limited method of the algorithmic classification tree, which consists in maintaining a criterion for stopping the procedure for constructing a tree model by the depth of the structure, optimizing its software implementations, introducing new types of algorithmic trees, as well as experimental studies of this method for a wider range of practical problems.

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Classification models of flood-related events based on algorithm trees

Cited by 2 publications

References 30 publications

The Method of Bounded Constructions of Logical Classification Trees in the Problem of Discrete Objects Classification

The Method of Bounded Constructions of Logical Classification Trees in the Problem of Discrete Objects Classification

Convergence Problem Schemes for Constructing Structures of Logical and Algorithmic Classification Trees

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