Abstract
The study is devoted to the development of an algorithm for classifying the work of queuing services based on cluster analysis with the refinement of clusters using a doubly stochastic model. The developed algorithm is compared with other known clustering/classification methods. Comparison is based on the labels set by experts for orders described by many parameters. The study identified the most significant parameters that were selected for the analysis of orders in queuing systems. The solution to the problem of predicting effective orders by the estimated parameters is obtained. The unsupervised learning approach using doubly stochastic autoregression proposed in the paper provided an increase in the accuracy in the classification task compared to traditional machine learning algorithms. The approach can be successfully used by queuing services to adjust pricing policy.
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This work was funded by the Russian Foundation for Basic Research under RFBR grant № 19-29-09048.
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Andriyanov, N., Dementiev, V., Tashlinskiy, A. (2022). Development and Research of Intellectual Algorithms in Taxi Service Data Processing Based on Machine Learning and Modified K-means Method. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_16
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