Abstract
The classification of data is a well-known and extensively studied scientific problem. When applying some procedure to a particular dataset, we have to consider many features related to the dataset and the technique applied. In the presented paper, we show the application of the Self-Optimizing Neural Network, which we will call it SONN, however with the remark that it cannot be confused with the Self-Organized NN. Our SONN can be in principle understood as a form of decision network with the reduced number of paths corresponding to every possible set of discretized values obtained by the special procedure from the real-valued data measured by the experimental setup. In the paper, we use the dataset obtained during the meteorological study in eastern Poland, which was burdened with the significant measurement error. The analysis, performed with various methods of determining of final signal as well as various metrics defined in the discretized space of solutions, shows that the proposed method can lead to visible improvement when compared to typical classification methods like SVM or Neural Networks.
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Miniak-Górecka, A., Podlaski, K., Gwizdałła, T. (2022). Self-Optimizing Neural Network in Classification of Real Valued Experimental Data. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_20
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