The Issue of Efficient Generation of Generalized Features in Algorithmic Classification Tree Methods

Povkhan, Igor; Lupei, Maksym; Kliap, Mykhailo; Laver, Vasyl

doi:10.1007/978-3-030-61656-4_6

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…The research is a continuation of the works devoted to the main problematic issues of the concept of tree-like classification schemes for discrete objects [6][7][8]11] in pattern recognition problems. The main attention in these works is paid to the problems of constructing, representing, using, and optimizing the structures of classification trees.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…We note a characteristic feature of classification tree models (LCT/ACT structures) to provide effective one-dimensional branching, which allows analyzing attributes, individual features in the object structure, and working with generalized features of various types. In this case, sets of generalized features can be represented as complex predicates, and for structures of algorithm trees in the form of sequences by autonomous classification and recognition algorithms [8]. This representation of classification models (LCT/ACT structures) is actively used for big data mining, where there is a need to build a classification model that predicts the value of one or more target variables based on data from the original TS array [19].…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Moreover, individual independent recognition algorithms estimated from TS data can be considered as branches. In this case, the output will be a new structurethe ACT model [8]. This paper will be devoted to ACT structures in practice.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…So, the coverage of the training set with a set of elementary features in the case of logical classification trees, or the coverage of the training set with a fixed set of autonomous recognition and classification algorithms in the case of algorithmic classification trees, generates a fixed tree-like data structure (a tree model) that enables compression and transformation of the initial data of the training set. This method allows significant software optimization, significant savings in the resources of the classification system [8] in problems with complex data structures. The scope of application of the concept of classification trees, various types of LCTs / ACTs structures is currently extremely voluminous.…”

Section: Introductionmentioning

confidence: 99%

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The Algorithm Tree Method in Solving the Task of Classifying Hydrographic Data

Povkhan

Міца

Mulesa

et al. 2022

RIC

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Context. The work is dedicated to the identification of a simple and effective mechanism by which it is possible to build algorithmic classification trees (algorithmic tree models) on the basis of fixed initial information in the form of a discrete data training sample. The constructed algorithmic classification tree will unmistakably classify (recognize) the entire training sample on which the model is built, have a minimum structure (structural complexity) and consist of components – autonomous classification and recognition algorithms as the vertices of the structure (attributes of the tree). Objective. The aim of this work is to create a simple, effective and universal method of constructing classification (recognition) models based on the concept of algorithmic trees for arrays of real hydrographic data, where the obtained schemes of classification systems (classification tree structure) are characterized by a tree structure (construction) and autonomous classification algorithms (sets of generalized features) as their structural elements (construction blocks). Method. The general scheme of synthesizing classification trees in a form of algorithmic trees on the basis of a procedure of approximation of an array of discrete data by a set of elementary classifiers, which for the set initial training sample builds a tree-like structure, i.e. a model of the algorithmic tree, is suggested. Moreover, the constructed scheme consists of a set of autonomous classification and recognition algorithms evaluated at each step/stage of constructing the classification tree for this initial sample. A method for constructing an algorithmic classification tree has been developed, the main idea of which is to approximate step-by-step the initial sample of an arbitrary volume and structure by a set of elementary classification algorithms. The method of algorithmic tree in the formation of the current algorithmic tree vertex, node, generalized feature provides selection of the most effective, highquality elementary classifiers from the initial set and completion of only those paths in the tree structure where the largest number of errors (failures) occurs. The structural complexity of the algorithmic tree design is estimated based on the number of transitions, vertices and tiers of the model structure, which allows one to improve the quality of its subsequent analysis, provide an effective decomposition mechanism, and build algorithmic tree structures under fixed constraint sets. The method of the algorithmic tree synthesis allows one to build different types of tree-like recognition models with different initial sets of elementary classifiers with predetermined accuracy for a wide class of problems of the artificial intelligence theory. Results. The developed method of building algorithmic tree models allows one to work with training samples of a large amount of different types of information (discrete data) and provides high speed and economy of hardware resources in the process of generating the final classification scheme, as well as to build classification trees with predetermined accuracy. Conclusions. An approach to the synthesis of new recognition algorithms (schemes) based on a library (set) of already known algorithms (methods) and schemes has been developed. That is, an effective scheme for recognizing discrete objects based on stepby-step evaluation and selection of classification algorithms (generalized features) at each step of the scheme synthesis is presented. Based on the suggested concept of algorithmic classification trees, a model of the structure of the algorithm tree was built, which provided classification of flood situations for the Uzh river basin.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Algorithm Tree Method in Solving the Task of Classifying Hydrographic Data

Povkhan

Міца

Mulesa

et al. 2022

RIC

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“…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. The disadvantage of such a scheme is the limitations on the structure of the initial training sample and the non-universality in applied terms.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Classification models of flood-related events based on algorithm trees

Povkhan

2020

EEJET

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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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The Issue of Efficient Generation of Generalized Features in Algorithmic Classification Tree Methods

Cited by 3 publications

References 19 publications

The Algorithm Tree Method in Solving the Task of Classifying Hydrographic Data

The Algorithm Tree Method in Solving the Task of Classifying Hydrographic Data

Classification models of flood-related events based on algorithm trees

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

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