IJISA Vol. 8, No. 8, 8 Aug. 2016

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Hybrid clustering-classification neural network, supervised/unsupervised learning, overlapping classes, diagnostics, reactive arthritis

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.

Yevgeniy Bodyanskiy, Olena Vynokurova, Volodymyr Savvo, Tatiana Tverdokhlib, Pavlo Mulesa, "Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.8, pp.1-9, 2016. DOI:10.5815/ijisa.2016.08.01

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