scholarly journals FEATURES OF SOFTWARE SOLUTIONS OF MODELS OF LOGICAL CLASSIFICATION TREES BASED ON SELECTION OF SETS OF ELEMENTARY FEATURES

2020 ◽  
pp. 72-90
Author(s):  
Igor Povkhan ◽  
Keyword(s):  
Artificial Intelligence ◽  
Decision Trees ◽  
Classification Tree ◽  
Classification Trees ◽  
Training Sample ◽  
Software System ◽  
Discrete Information ◽  
Wide Range ◽  
Logical Tree ◽  
Selection Of

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.

scholarly journals Estimation of the complexity of constructing a logical classification tree for an arbitrary case in conditions of strong class separation of the initial training sample

2020 ◽  
Vol 68 (3) ◽  
Author(s):  
I. F. Povkhan ◽  
Keyword(s):  
Decision Trees ◽  
Classification Tree ◽  
Classification Trees ◽  
Training Sample ◽  
Tree Structure ◽  
Classification Model ◽  
Initial Training ◽  
Tree Models ◽  
Logical Tree ◽  
Selection Of

The paper offers an estimation of the complexity of the constructed logical tree structure for classifying an arbitrary case in the conditions of a strong class division of the initial training sample. The principal solution to this question is of a defining nature, regarding the assessment of the structural complexity of classification models (in the form of tree-like structures of LCT/ACT) of discrete objects for a wide range of applied classification and recognition problems in terms of developing promising schemes and methods for their final optimization (minimization) of post-pruning structure. The presented research is relevant not only for constructions (structures) of logical classification trees, but also allows us to extend the scheme of complexity estimation to the General case of algorithmic structures (ACT models) of classification trees (the concept of algorithm trees and trees of generalized features - TGF). Is investigated the actual question of the concept of decision trees (tree recognition) – evaluation of the maximum complexity of the General scheme of constructing a logical tree based classification procedure of stepwise selection of sets of elementary features (they can be diverse sets and combinations) that for given initial training sample (array of discrete information) builds a tree structure (classification model), from a set of elementary features (basic attributes) are estimated at each stage of the scheme of the model in this sample for the case of strong separation of classes. Modern information systems and technologies based on mathematical approaches (models) of pattern recognition (structures of logical and algorithmic classification trees) are widely used in socio-economic, environmental and other systems of primary analysis and processing of large amounts of information, and this is due to the fact that this approach allows you to eliminate a set of existing disadvantages of well-known classical methods, schemes and achieve a fundamentally new result. The research is devoted to the problems of classification tree models (decision trees), and offers an assessment of the complexity of logical tree structures (classification tree models), which consist of selected and ranked sets of elementary features (individual features and their combinations) built on the basis of the General concept of branched feature selection. This method, when forming the current vertex of the logical tree (node), provides the selection of the most informative (qualitative) elementary features from the source set. This approach allows you 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 instrumental analysis (the final decomposition of the model).

scholarly journals Estimation of general complexity of the procedure for constructing a binary logical classification tree for an arbitrary case

2020 ◽  
Vol 69 (2) ◽  
Author(s):  
I. F. Povkhan ◽  
Keyword(s):  
General Procedure ◽  
Classification Tree ◽  
Classification Trees ◽  
Training Sample ◽  
Classification Model ◽  
Tree Structures ◽  
Wide Range ◽  
Tree Models ◽  
Logical Tree ◽  
Selection Of

We propose an upper estimate of the complexity of the binary logical tree synthesis procedure for classifying an arbitrary case (for conditions of weak and strong separation of classes in the training sample). The solution to this question is of a fundamental nature, regarding the assessment of the structural complexity of classification models (in the form of tree structures) of discrete objects for a wide range of applied classification and recognition problems in terms of developing promising schemes and methods for their final optimization (minimization) of the structure. This research is relevant not only for the constructions of logical classification trees, but also allows us to extend the complexity estimation scheme itself to the general case of algorithmic structures of classification trees (concepts of algorithm trees and generalized feature trees). The current issue of complexity of the general procedure for constructing a logical classification tree based on the concept of step-by-step selection of sets of elementary features (their possible heterogeneous sets and combinations), which for a given initial training sample (an array of discrete information) builds a tree structure (classification model), from a set of elementary features (basic attributes) evaluated at each stage of the model construction scheme for this sample. Thus, modern information technologies based on mathematical models of pattern recognition (logical and algorithmic classification trees) are widely used in socio-economic, environmental and other systems of primary analysis and processing of large amounts of information. This is due to the fact that this approach allows you to eliminate a set of existing disadvantages of well-known classical methods and schemes and achieve a fundamentally new result. The work is devoted to the problems of classification tree models (decision trees), and offers an assessment of the complexity of logical tree structures (classification tree models), which consist of selected and ranked sets of elementary features built on the basis of the General concept of branched feature selection. This method, when forming the current vertex of the logical tree (node), provides the selection of the most informative (qualitative) elementary features from the source set. This approach allows you 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.

scholarly journals LOGICAL CLASSIFICATION TREES IN RECOGNITION PROBLEMS

2020 ◽  
Vol 10 (2) ◽  
pp. 12-15
Author(s):  
Igor Povhan
Keyword(s):  
Classification Tree ◽  
Classification Trees ◽  
Large Data ◽  
Training Sample ◽  
Large Data Sets ◽  
Data Sets ◽  
Complex Structures ◽  
Training Set ◽  
Logical Tree ◽  
And Storage

The paper is dedicated to algorithms for constructing a logical tree of classification. Nowadays, there exist many algorithms for constructing logical classification trees. However, all of them, as a rule, are reduced to the construction of a single classification tree based on the data of a fixed training sample. There are very few algorithms for constructing recognition trees that are designed for large data sets. It is obvious that such sets have objective factors associated with the peculiarities of the generation of such complex structures, methods of working with them and storage. In this paper, we focus on the description of the algorithm for constructing classification trees for a large training set and show the way to the possibility of a uniform description of a fixed class of recognition trees. A simple, effective, economical method of constructing a logical classification tree of the training sample allows you to provide the necessary speed, the level of complexity of the recognition scheme, which guarantees a simple and complete recognition of discrete objects.

Methodical approach to assess the condition of samples of weapons and military equipment on the basis of algorithmic trees

2021 ◽  
pp. 69-76
Author(s):  
V. Dudnyk ◽  
O. Grishchyn ◽  
V. Netrebko ◽  
R. Prus ◽  
M. Voloshcuk
Keyword(s):  
Classification Scheme ◽  
Classification Tree ◽  
Structural Complexity ◽  
Classification Trees ◽  
Training Sample ◽  
Technical Condition ◽  
Tree Model ◽  
Military Equipment ◽  
Classification Tree Model ◽  
Types Of Information

An effective mechanism for the synthesis of classification trees based on fixed initial information (in the form of a training sample) for the task of recognizing the technical condition of samples of weapons and military equipment. The constructed algorithmic classification tree (model) will unmistakably classify (recognize) the entire training sample (situational objects) according to which the classification scheme is constructed. And have a minimal structure (structural complexity) and consist of components (modules) - autonomous algorithms for classification and recognition as vertices of the structure (attributes of the tree). The developed method of building models of algorithm trees (classification schemes) allows you to work with training samples of a large amount of different types of information (discrete type). Provides high accuracy, speed and economy of hardware resources in the process of generating the final classification scheme, build classification trees (models) with a predetermined accuracy. The approach of synthesis of new algorithms of recognition (classification) on the basis of library (set) of already known algorithms (schemes) and methods is offered. Based on the proposed concept of algorithmic classification trees, a set of models was built, which provided effective classification and prediction of the technical condition of samples. The paper proposes a set of general indicators (parameters), which allows to effectively present the general characteristics of the classification tree model, it is possible to use it to select the most optimal tree of algorithms from a set based on methods of random classification trees. Practical tests have confirmed the efficiency of mathematical software and models of algorithm trees.

scholarly journals THE USE OF ARTIFICIAL INTELLIGENCE METHODS FOR APPROXIMATION OF THE MECHANICAL BEHAVIOR OF RUBBER-LIKE MATERIALS

2021 ◽  
pp. 95-99
Author(s):  
Oleksii Vodka ◽  
Serhii Pohrebniak
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Hysteresis Loops ◽  
Test Sample ◽  
Training Sample ◽  
The Neural Network ◽  
Internal Structures ◽  
Wide Range ◽  
Software Product ◽  
Direct Distribution

In the XXI century, neural networks are widely used in various fields, including computer simulation and mechanics. This popularity is due to the factthat they give high precision, work fast and have a very wide range of settings. The purpose of creating a software product using elements of artificialintelligence, for interpolation and approximation of experimental data. The software should work correctly, and yield results with minimal error. Thedisadvantage of using mathematical approaches to calculating and predicting hysteresis loops is that they describe unloading rather poorly, thus, weobtain incorrect data for calculating the stress-strain state of a structure. The solution tool use of elements of artificial intelligence, but rather neuralnetworks of direct distribution. The neural network of direct distribution has been built and trained in this work. It has been trained with a teacher (ateacher using the method of reverse error propagation) based on a learning sample of a pre-experiment. Several networks of different structures werebuilt for testing, which received the same dataset that was not used during the training, but was known from the experiment, thus finding a networkerror in the amount of allocated energy and in the mean square deviation. The article describes in detail the mathematical interpretation of neuralnetworks, the method for training them, the previously conducted experiment, structure of network that was used and its topology, the training method,preparation of the training sample, and the test sample. As a result of the robots carried out, the software was tested in which an artificial neuralnetwork was used, several types of neural networks with different input data and internal structures were built and tested, the error of their work wasdetermined, the positive and negative sides of the networks that were used were formed.

Robotic mounted rail arm system for implementing effective workplace safety for migrant workers

Work ◽  
2021 ◽  
pp. 1-8
Author(s):  
Hongbo Wei ◽  
Md Arafatur Rahman ◽  
Xu Hu ◽  
Lin Zhang ◽  
Lieyan Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Migrant Workers ◽  
Workplace Safety ◽  
Robotic Systems ◽  
Efficiency Ratio ◽  
Cycle Times ◽  
Technological Advances ◽  
Wide Range ◽  
Ongoing Project ◽  
Selection Of

BACKGROUND: The selection of orders is the method of gathering the parts needed to assemble the final products from storage sites. Kitting is the name of a ready-to-use package or a parts kit, flexible robotic systems will significantly help the industry to improve the performance of this activity. In reality, despite some other limitations on the complexity of components and component characteristics, the technological advances in recent years in robotics and artificial intelligence allows the treatment of a wide range of items. OBJECTIVE: In this article, we study the robotic kitting system with a Robotic Mounted Rail Arm System (RMRAS), which travels narrowly to choose the elements. RESULTS: The objective is to evaluate the efficiency of a robotic kitting system in cycle times through modeling of the elementary kitting operations that the robot performs (pick and room, move, change tools, etc.). The experimental results show that the proposed method enhances the performance and efficiency ratio when compared to other existing methods. CONCLUSION: This study with the manufacturer can help him assess the robotic area performance in a given design (layout and picking a policy, etc.) as part of an ongoing project on automation of kitting operations.

Artificial intelligence in the modeling of chemical reactions kinetics

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Maciej Staszak
Keyword(s):  
Artificial Intelligence ◽  
Chemical Processes ◽  
Rate Equations ◽  
Network Systems ◽  
Current State ◽  
Wide Range ◽  
Kinetics Of ◽  
Fast Evolution ◽  
Selection Of

AbstractThe work presents a selection of recent papers in the field of modeling chemical kinetics by the use of artificial intelligence methods. Due to the fact that kinetics of the chemical reaction is the key element of industrial reactor design and analysis, the work is focused on the presentation of the quality of modeling, the assembly of neural network systems and methods of training required to achieve acceptable results. The work covers a wide range of classes of chemical processes and modeling approaches presented by several authors. Because of the fact that the methods of neural networks training require huge amounts of data, many approaches proposed are intrinsically based on classical kinetics modeling like Monte Carlo methods, quantum ab initio models or classical Arrhenius-like approaches using mass balance rate equations. The work does not fully exhaust the area of artificial intelligence because of its very broad scope and very fast evolution, which has been greatly accelerated recently. However, it is a contribution to describing the current state of science in this field.

scholarly journals A Method for the Automatic Selection of Training Tasks in Learning Environment for IT Students

2020 ◽  
Vol 24 (2) ◽  
pp. 17-28 ◽  
Author(s):  
S. U. Rzheutskaya ◽  
M. V. Kharina
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Learning Process ◽  
Learning Effect ◽  
Training Sample ◽  
Educational Process ◽  
Information Environment ◽  
Level Of Difficulty ◽  
Selection Of

Purpose of research. The research, the results of which are presented in this article, was carried out in order to activate and improve the efficiency of independent work of students in the information environment of learning by rational individual selection of training tasks. In the process of the research, a method for automatically selecting tasks for self-completion was developed and implemented in the educational process, based on predicting the difficulty and learning effect of the task for a specific student, taking into account the complexity of the task and the student’s readiness to perform this task. Methods and materials. The article provides a distinction between the concepts of complexity, difficulty, and the learning effect of training tasks. On this basis, the task of predicting the level of difficulty of the task for the student is set as a task of automatic classification of “student-task" pairs, which represent a set of characteristics of the student and the task that are available in the database of the e-learning system. The result of the classification is a forecast of the level of difficulty of the task for the student, on the basis of which a decision is made about the learning effect of this task.The classification problem is one of the well-developed machine learning tasks “with a lecturer". Decision trees were selected from several well-known trained classification models for implementation, since they, unlike neural networks, represent prediction rules in a visual form, while highlighting significant features. The learning phase of the model consists of building a decision tree based on a training sample containing data on precedents for students to complete tasks. As a result of the computational experiment, decision trees were built for several disciplines that practice automatic verification of students’ decisions, i.e. there is data for forming a training sample.Results. The article provides an example of a decision tree based on a training sample, which is formed on the basis of data from an electronic workshop on the discipline “Foreign language ". The quality of the predictive model was determined on the exam sample by the criteria of accuracy and generalizing ability (the degree of severity of the “retraining effect”). The obtained values of these indicators allow us to recognize the quality as acceptable. The first results ofpractical application of the proposed method of selecting tasks in the educational process are analyzed. The software developed in the process of the research can be considered as the basis of a recommendation system that can not replace live communication between the student and the lecturer, but is their smart assistant in the learning process. Conclusion. In general, the results of the research show that the capabilities of artificial intelligence technologies, in particular, machine learning, allow us to put into practice the principle of individualized learning, to adapt the learning process to the individual characteristics of each student in order to effectively develop their professional competencies. The proposed method is implemented and tested in the information environment of training students of IT areas of Vologda State University, however, this approach is quite universal, and it can be extended to other subject areas and forms of training.

scholarly journals Robust optimal classification trees under noisy labels

2021 ◽  
Author(s):  
Victor Blanco ◽  
Alberto Japón ◽  
Justo Puerto
Keyword(s):  
Classification Tree ◽  
Classification Trees ◽  
Training Sample ◽  
Mixed Integer ◽  
Computational Results ◽  
Non Linear Programming ◽  
Detection Techniques ◽  
Linear Programming Formulation ◽  
Optimal Classification ◽  
Noisy Labels

AbstractIn this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample. The motivation of this new methodology is based on the superaditive effect of combining together margin based classifiers and outlier detection techniques. Our approach rests on two main elements: (1) the splitting rules for the classification trees are designed to maximize the separation margin between classes applying the paradigm of SVM; and (2) some of the labels of the training sample are allowed to be changed during the construction of the tree trying to detect the label noise. Both features are considered and integrated together to design the resulting Optimal Classification Tree. We present a Mixed Integer Non Linear Programming formulation for the problem, suitable to be solved using any of the available off-the-shelf solvers. The model is analyzed and tested on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of our approach. Our computational results show that in most cases the new methodology outperforms both in accuracy and AUC the results of the benchmarks provided by OCT and OCT-H.

scholarly journals THE METHOD OF BOUNDED CONSTRUCTIONS OF LOGICAL CLASSIFICATION TREES IN THE PROBLEM OF DISCRETE OBJECTS CLASSIFICATION

2021 ◽  
Vol 3 (1) ◽  
pp. 22-29
Author(s):  
I. F. Povkhan ◽  
Keyword(s):  
Classification Tree ◽  
Feature Selection Method ◽  
Structural Complexity ◽  
Classification Trees ◽  
Real Data ◽  
Training Sample ◽  
Tree Structure ◽  
Tree Model ◽  
Classification Tree Model ◽  
Proposed Modification

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.

Sign in / Sign up

Export Citation Format

Share Document