Fuzzy Recurrent Mappings in Multiagent Simulation of Population Dynamics Systems

Chumachenko, Dmytro; Sokołov, Oleksandr; Yakovlev, Sergiy

doi:10.47839/ijc.19.2.1773

Cited by 16 publications

(2 citation statements)

References 17 publications

(20 reference statements)

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“…All this requires specialized approaches, methods, and an understanding of the specifics of the medical context to achieve reliable and valuable results when analyzing biomedical data sets of small volumes. That is why, to increase the accuracy of classification in biomedical engineering, new hybrid methods of data analysis are constantly being developed, which include machine learning, fuzzy logic [9], deep learning [10], kernel methods [11], and statistical approaches [12].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised-Supervised Ensemble Technology With Non-Iterative Training Algorithm for Small Biomedical Data Analysis

IZONIN

2023

CSIT

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Improving the accuracy of intelligent data analysis is an important task in various application areas. Existing machine learning methods do not always provide a sufficient level of classification accuracy for their use in practice. That is why, in recent years, hybrid ensemble methods of intellectual data analysis have begun to develop. They are based on the combined use of clustering and classification procedures. This approach provides an increase in the accuracy of the classifier based on machine learning due to the expansion of the space of the input data of the task by the results of the clustering. In this paper, the tasks of modification and improvement of such technology for small data analysis are considered. The basis of the modification is the use of clustering with output at the first step of the method to increase the accuracy of the entire technology. Despite the high accuracy of the work, this approach requires a significant expansion of the inputs of the final linear classifier (labels of the obtained clusters are added to the initial inputs). To avoid this shortcoming, the paper proposes an improvement based on the introduction of a new classification procedure at the first step of the method and replaces all the initial inputs of the task with the results of its work. In parallel with it, clustering is performed taking into account the original attribute, the results of which are added to the output of the classifier of the first step. In this way, the formation of an expanded set of data of significantly lower dimensionality in comparison with the existing method takes place (here there is no longer a large number of initial features, which is characteristic of biomedical engineering tasks). This reduces the training time of the method and increases its generalization properties. Modeling of the method was based on the use of a short dataset contained in an open repository. After the preprocessing procedures, the dataset has only 294 vectors, each of which was characterized by 18 attributes. Data classification was done using an SGTM-based neural-like structure classifier. This linear classifier provides high accuracy of work. In addition, it does not provide for the implementation of an iterative training procedure and additional adjustment of work parameters. Data clustering was performed using the k-means method. This choice is due to both the simplicity and speed of its work. The search for the optimal number of k-means clusters was carried out using 4 different methods. They all showed different results. That is why, some experiments were conducted to assess the influence of different numbers of clusters (from 3 to 7) on the accuracy of all 4 algorithms of the developed technology. The accuracy of the proposed technology has been established experimentally in comparison with the linear classifier and the existing hybrid method. In addition, by reducing the inputs of the final classifier, the developed technology reduces the duration of the training procedure compared to the basic method. All this ensures the possibility of using the proposed technology when solving various applied problems of medical diagnostics, in particular, based on the analysis of small data. Keywords: small data approach, non-iterative training, ensemble learning, unsupervised-supervised technology, biomedical engineering.

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Section: Introductionmentioning

confidence: 99%

An Unsupervised-Supervised Ensemble Technology With Non-Iterative Training Algorithm for Small Biomedical Data Analysis

IZONIN

2023

CSIT

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“…As far as large amounts of data are concerned, the development of a modern neural network toolkit for data flow processing allows of fulfilling these tasks with high accuracy [5,6]. In case of ambiguity or inaccuracy of information, the tools of fuzzy logic should be used [4,10]. In particular, the use of linguistic variables makes it possible to take into account all the information available about the object of research when solving various application tasks.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Diagnostics Systems Based on SGTM Neural-Like Structure and T-Controller

Tkachenko

Izonin

Tkachenko

2021

Lecture Notes on Data Engineering and Communications Technologies

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Neuro-fuzzy models of management nowadays are becoming more widespread in various industries. Many papers deal with the synthesis of neuro-fuzzy models of diagnostics in economics, medicine, and industrial tasks. Most of them are based on iterative topologies of artificial neural networks and traditional fuzzy inference systems. The latter ones do not always ensure high accuracy, which affects the entire system that is being developed. This paper presents a new neuro-fuzzy diagnostic system based on non-iterative ANN and a new fuzzy model, a T-controller. The flowchart of the system proposed is given. All the operation stages of the system are described in detail, from the collection and preliminary processing of data to two different stages of diagnostics. The last stage can be performed manually or using a T-controller. Simulation of the system is conducted by means of a real set of data. The task was to predict the generator power based on a set of 13 independent variables. To improve the accuracy of the system performance, the Padé polynomial has been used, the coefficients of which are synthesized on the basis of a pre-trained SGTM neural-like structure using an optimization simulated annealing simulation. High accuracy of the system performance is shown using various performance indicators. The coefficients of the required polynomial are synthesized, which allows of fulfilling the task in a more understandable form.

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On Using Information and Communication Technologies in Process of Mathematical Specialties Education

Padalko

2021

Lecture Notes in Networks and Systems

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The study examines the pedagogical conditions for the use of information and communication technologies in the process of professional training of students of mathematical specialties. It is proved that the use of information and communication technologies in the educational process makes it possible to increase the indicators of the formation of students’ professional competence. The analysis of the results of experimental studies made it possible to draw a conclusion about the advisability of using information and communication technologies for the formation of professional competence of students of mathematical specialties.

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Fuzzy Recurrent Mappings in Multiagent Simulation of Population Dynamics Systems

Cited by 16 publications

References 17 publications

An Unsupervised-Supervised Ensemble Technology With Non-Iterative Training Algorithm for Small Biomedical Data Analysis

An Unsupervised-Supervised Ensemble Technology With Non-Iterative Training Algorithm for Small Biomedical Data Analysis

Neuro-Fuzzy Diagnostics Systems Based on SGTM Neural-Like Structure and T-Controller

On Using Information and Communication Technologies in Process of Mathematical Specialties Education

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