Please use this identifier to cite or link to this item: http://openarchive.nure.ua/handle/document/6908
Title: Adaptive human machine interaction approach for feature selection-extraction task in medical data mining
Authors: Perova, Iryna
Bodyanskiy, Yevgeniy
Keywords: Human Machine Interaction
feature selection
feature extraction
Medical Data Mining
Oja’s neuron
Neural Network Approach
Issue Date: 2018
Publisher: International Journal of Computing
Citation: Perova I. Adaptive human machine interaction approach for feature selection-extraction task in medical data mining / I. Perova, Ye. Bodyanskiy // International Journal of Computing. - 2018.-17 (2). - P. 113-119.
Abstract: 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.
URI: http://openarchive.nure.ua/handle/document/6908
Appears in Collections:Кафедра біомедичної інженерії (БМІ)

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