The General Concept of the Methods of Algorithmic Classification Trees

Povkhan, Igor

doi:10.15588/1607-3274-2020-3-10

Cited by 2 publications

(8 citation statements)

References 5 publications

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“…Presented study allows overcoming these system limitations of constructing the recognition systems by synthesizing the algorithmic trees of classification. Their main specific feature is the modular tree structure and the possibility of applying an arbitrary recognition algorithm or method in the process of synthesizing the classification scheme [9][10][11].…”

Section: Nomenclature N M Is a Manifold Of Real Vectors Of Dimensionality N ;mentioning

confidence: 99%

Problem of a Discrete Data Array Approximation by a Set of Elementary Geometric Algorithms

Povkhan

Міца

Mulesa

et al. 2021

RIC

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Context. In this paper, a problem of a discrete data array approximation by a set of elementary geometric algorithms and a recognition model representation in a form of algorithmic classification tree has been solved. The object of the present study is a concept of a classification tree in a form of an algorithm trees. The subject of this study are the relevant models, methods, algorithms and schemes of different classification tree construction. Objective. The goal of this work is to create a simple and efficient method and algorithmic scheme of building the tree-like recognition and classification models on the basis of the algorithm trees for training selections of large-volume discrete information characterized by a modular structure of independent recognition algorithms assessed in accordance with the initial training selection data for a wide class of applied tasks. Method. A scheme of classification tree (algorithm tree) synthesis has been suggested being based on the data array approximation by a set of elementary geometric algorithms that constructs a tree-like structure (the ACT model) for a preset initial training selection of arbitrary size. The latter consists of a set of autonomous classification/recognition algorithms assessed at each step of the ACT construction according to the initial selection. A method of the algorithmic classification tree construction has been developed with the basic idea of step-by-step arbitrary-volume and structure initial selection approximation by a set of elementary geometric classification algorithms. When forming a current algorithm tree vertex, node and generalized attribute, this method provides alignment of the most effective and high-quality elementary classification algorithms from the initial set and complete construction of only those paths in the ACT structure, where the most of classification errors occur. The scheme of synthesizing the resulting classification tree and the ACT model developed allows one to reduce considerably the tree size and complexity. The ACT construction structural complexity is being assessed on the basis of a number of transitions, vertices and tiers of the ACT structure that allows the quality of its further analysis to be increased, the efficient decomposition mechanism to be provided and the ACT structure to be built in conditions of fixed limitation sets. The algorithm tree synthesis method allows one to construct different-type tree-like recognition models with various sets of elementary classifiers at the preset accuracy for a wide class of artificial intelligence theory problems. Results. The method of discrete training selection approximation by a set of elementary geometric algorithms developed and presented in this work has received program realization and was studied and compared with those of logical tree classification on the basis of elementary attribute selection for solving the real geological data recognition problem. Conclusions. Both general analysis and experiments carried out in this work confirmed capability of developed mechanism of constructing the algorithm tree structures and demonstrate possibility of its promising use for solving a wide spectrum of applied recognition and classification problems. The outlooks of the further studies and approbations might be related to creating the othertype algorithmic classification tree methods with other initial sets of elementary classifiers, optimizing its program realizations, as well experimental studying this method for a wider circle of applied problems.

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Section: Nomenclature N M Is a Manifold Of Real Vectors Of Dimensionality N ;mentioning

confidence: 99%

Problem of a Discrete Data Array Approximation by a Set of Elementary Geometric Algorithms

Povkhan

Міца

Mulesa

et al. 2021

RIC

View full text Add to dashboard Cite

show abstract

“…Study [9] proposes a scheme of generating the LCT structure based on a stepwise selection of elementary attributes, the disadvantage of which is the heavy dependence of model complexity on the effectiveness of the final minimization, the procedure of tree pruning. Papers [10,11] suggest a modular scheme to build classifiers in the form of classification tree structures, which makes it possible to circumvent the limitations of conventional decision tree methods. Work [12] proposes an effective scheme for generating generalized features based on constructing the sets of hyperparallelepipeds.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…There is a debatable issue related to the effective minimization of the structure of the built model of the classification tree. An important task tackled in work [10] is the issue of synthesis of recognition trees, which would actually be represented by the tree of algorithms. This approach could make it possible to construct new classifiers based on a modular principle.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The main idea underlying the methods and algorithms for branching the selection of features, and vertices of ACT algorithms can be defined as the optimal approximation of some initial TS by a set of ranked classification algorithms. Then there is a central issue from [10]: the task to choose an effective branching criterion, the selection of vertices, attributes, signs of discrete objects for LCT schemes and ACT algorithms. These fundamental tasks are considered by the authors of [33] when addressing the issues related to the qualitative assessment of individual discrete features, their sets, and fixed combinations, which makes it possible to introduce an effective mechanism for the implementation of branching in the structure of a logical tree.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The concept of classification trees makes it possible to use the combination of attributes and their sets as the signs, the vertices of a classification tree structure. If one dives deeper and disregards the attributes of objects as branching while selecting individual independent recognition algorithms, evaluated according to TS, a new structure of the ACT would be obtained at the output [10,11]. Thus, there is a fundamental need to construct an effective method based on the concept of decision trees for recognizing large data sets while rationally utilizing the already accumulated potential of methods and classification schemes.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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The General Concept of the Methods of Algorithmic Classification Trees

Cited by 2 publications

References 5 publications

Problem of a Discrete Data Array Approximation by a Set of Elementary Geometric Algorithms

Problem of a Discrete Data Array Approximation by a Set of Elementary Geometric Algorithms

Classification models of flood-related events based on algorithm trees

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