Перегляд за автором "Perova, I."
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Публікація Adaptive human machine interaction approach for feature selection-extraction task in medical data mining(International Journal of Computing, 2018) Perova, I.; Bodyanskiy, Ye. V.Feature Selection task is one of the most complicated and actual in the areas of Data Mining and Human Machine Interaction. Many approaches to its solving are based on non-mathematical and presentative hypothesis. New approach to evaluation of medical features information quantity, based on optimized combination of feature selection and feature extraction methods is proposed. This approach allows us to produce optimal reduced number of features with linguistic interpreting of each of them. Hybrid system of feature selection/extraction based on Neural Network-Physician interaction is investigated. This system is numerically simple, can produce feature selection/extraction with any number of factors in online mode using neural network-physician interaction based on Oja’s neurons for online principal component analysis and calculating distance between first principal component and all input features. A series of experiments confirms efficiency of proposed approaches in Medical Data Mining area and allows physicians to have the most informative features without losing their linguistic interpreting.Публікація Evolving flexible neuro-fuzzy system for medical diagnostic tasks(IJCSMC, 2015) Turuta, O.; Perova, I.; Deineko, A. O.In the paper architecture and training method for evolving flexible diagnostic neuro-fuzzy-system are investigated. The proposed system is simple in numeric realization and characterized by a high learning rate and flexibility, that make possible to use it in conditions of small training sets and on big data sets, coming to processing in online-mode.Публікація Evolving Neural Network for Kernel Principal Component Analysis(IJCSMC, 2015) Turuta, O.; Deineko, A. O.; Perova, I.; Kutsenko, Y.; Shalamov, M.In the paper kernel evolving neural network and its learning algorithm are investigated. The proposed system solves the problem of finding the eigenvectors and the corresponding principal components in on-line mode in an environment where hidden in the experimental data interdependencies are nonlinear and can change throw time.Публікація Fast medical diagnostics using autoassociative neuro-fuzzy memory(International Journal of Computing, 2017) Perova, I.; Bodyanskiy, Ye. V.This paper proposes an architecture of fast medical diagnostics system based on autoassociative neuro-fuzzy memory. The architecture of proposed system is close to traditional Takagi-Sugeno-Kang neuro-fuzzy system, but it is based on other principles. This system contains of recording subsystem and pattern retrieval subsystem, where diagnostics of patients with unknown diagnoses is realized. Level of memberships for all other possible diagnoses from recording subsystem is determined too. System tuning is based on lazy learning procedure and “neurons in data points” principle and uses bell-shaped fuzzy basis functions. Number of these functions changes during training process using principles of evolving connectionist systems. Bell-shaped membership functions centers can be tuned using proposed algorithm, processes of accumulation patients in fundamental memory and patients retrieval are described. This hybrid neuro-fuzzy associative memory combines advantages of fuzzy inference systems, artificial neural networks and evolving systems and its using provides the increasing of autoassociative memories capacity without essential complication of its architecture for medical diagnostics tasks.Публікація Medical online neuro-fuzzy diagnostics system with active learning(International Journal of Advances in Computer and Electronics Engineering, 2017) Bodyanskiy, Ye. V.; Perova, I.Situations when in the medical data set some patients have known diagnoses and all other have unknown ones is spread wise problem of present-day medicine. Known systems of computational intelligence show mediocre level of diagnostics in these data sets. In this paper online neuro-fuzzy diagnostics system with active learning is proposed. This system allows to increase a quality of medical diagnostics under the condition of small number of known reference signals due to combination of special learning algorithms – active learning. The proposed online neuro-fuzzy system is based on popular neural networks as Self-Organizing Map (SOM) and Learning Vector Quantization network (LVQ). Active learning procedure permits to tune their synaptic weights using simple recurrent self-learning procedures (SOM) and controlled learning with teacher (LVQ). Neuro-fuzzy diagnostics system with active learningwas used for breast cancer in Wisconsin data set processing and showed higher level of classification-clusterization results comparatively with known systemsПублікація Online fuzzy clustering of high dimension data streams based on neural network ensembles(Innovative Technologies and Scientific Solutions for Industries, 2019) Bodyanskiy, Ye. V.; Perova, I.; Zhernova, P.The subject matter of the article is fuzzy clustering of high-dimensional data based on the ensemble approach, provided that a number and shape of clusters are not known. The goal of the work is to create the neuro-fuzzy approach for clustering data when the data stream is fed for online processing and a number and shape of clusters are unknown. The following tasks are solved in the article - the input feature space is compressed in the online mode; the model of neural network ensembles for data clustering is built; the ensemble of neuro-fuzzy networks for clustering high-dimensional data is developed; the approach for clustering data in the online mode is worked out. The following results are obtained - the main idea of the proposed approach is based on a modification of the fuzzy C-means algorithm. To reduce the dimension of the input space, the modified Hebb-Sanger network is suggested to be used; this net is characterized by the increased speed and is built on the basis of the modified Oja neurons. A speed-optimized learning algorithm for the Oja neuron is proposed. Such a network implements the method of principal components in the online mode with high speed. Conclusions. In the event the reduction-compression procedure cannot be used due to the probability of losing the physical meaning of the original space, a new clustering criterion was introduced; this criterion contains both a well-known polynomial fuzzifier and the weighment of individual components of the deviations of presented images from cluster centroids. The recurrent modification based on the algorithms proposed in this article is introduced. A mathematical model is developed to determine the quality of clustering with the use of the Xi-Beni index, which was modified for the online mode. The experimental results confirm the fact that the proposed system enables solving a wide range of Data Mining tasks when data sets are processed online, provided that a number and shape of clusters are not known and there is a large number of observations as well.Публікація Online Medical Data Stream Mining Based on Adaptive Neuro-Fuzzy Approaches(Cybernetics and computer engineering, 2019) Perova, I.; Bodyanskiy, Ye. V.Data mining approaches in medical diagnostics tasks have a number of special properties that do not allow the use of such approaches in a classical form. That’s why adaptive neuro-fuzzy systems for online medical data stream processing tasks and its learning algorithms have been developed. Proposed systems can process medical data streams in three modes: supervised learning, unsupervised learning and active learning. The purpose of the paper is to develop approach, based on adaptive neuro-fuzzy systems to solve the tasks of medical data stream mining in online-mode.