Abstract
The paper describes a classification algorithm based on the use of the nearest neighbor graph, that is built on the elements of both train and test sets. Process of classification is based on the frequency of elements of each class in the communities detected in the graph. Accuracy of classification for the developed algorithm exceeds accuracy of classification for random forest algorithm and XGBoost on particular datasets.
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Chernoskutov, M. (2022). Using Graphs for Classification. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 217. Springer, Singapore. https://doi.org/10.1007/978-981-16-2102-4_67
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DOI: https://doi.org/10.1007/978-981-16-2102-4_67
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