Studies on Neural Networks as a Fusion Method for Dispersed Data with Noise

Małgorzata Przybyła-Kasperek () and Kwabena Frimpong Marfo ()
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Małgorzata Przybyła-Kasperek: University of Silesia in Katowice, Institute of Computer Science
Kwabena Frimpong Marfo: University of Silesia in Katowice, Institute of Computer Science

A chapter in Advances in Information Systems Development, 2023, pp 169-186 from Springer

Abstract: Abstract In this paper, the issues of classification based on dispersed data are considered. For this purpose, an approach is used in which prediction vectors are generated locally using the k-nearest neighbors classifier. However, in central server, the final fusion of prediction vectors is made with the use of a neural network. The main aim of the study is to check the influence of noise intensity, various data characteristics (the number of conditional attributes, the number of objects, the number of decision classes) and the degree of dispersion on the quality of classification of the considered approach. For this purpose, 270 data sets were generated that differed by the above factors. It was found that each of the examined factors has a statistically significant impact on the quality of classification. The main conclusions are as follows. For dispersed data, multidimensionality is very good. The greater the dispersion in data, the worse the quality of classification. Only when the noise intensity significantly increased, we can observe a significant increase in the classification error in comparison with the lower noise level. This means that the classification method for dispersed data with neural network is immune to noise to some extent.

Date: 2023
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DOI: 10.1007/978-3-031-32418-5_10

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