Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis
Автор: Yevgeniy Bodyanskiy, Olena Vynokurova, Volodymyr Savvo, Tatiana Tverdokhlib, Pavlo Mulesa
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 8 vol.8, 2016 года.
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In the paper, the hybrid clustering-classification neural network is proposed. This network allows to increase a quality of information processing under the condition of overlapping classes due to the rational choice of learning rate parameter and introducing special procedure of fuzzy reasoning in the clustering-classification process, which occurs both with external learning signal ("supervised"), and without one ("unsupervised"). As similarity measure neighborhood function or membership one, cosine structures are used, which allow to provide a high flexibility due to self-learning-learning process and to provide some new useful properties. Many realized experiments have confirmed the efficiency of proposed hybrid clustering-classification neural network; also, this network was used for solving diagnostics task of reactive arthritis.
Hybrid clustering-classification neural network, supervised/unsupervised learning, overlapping classes, diagnostics, reactive arthritis
Короткий адрес: https://sciup.org/15010843
IDR: 15010843
Список литературы Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis
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