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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References
Kozodoi, N., Jacob, J., Lessmann, S.: Fairness in credit scoring: Assessment, implementation and profit implications. CoRR arXiv preprint, arXiv:2103.01907v3 (2021)
Niu, B., Ren, J., Li, X.: Credit scoring using machine learning by combing social network information: evidence from peer-to-peer lending. Information 10, 397 (2019)
Zhanga, Y., Chia, G., Zhanga, Z.: Decision tree for credit scoring and discovery of significant features: an empirical analysis based on Chinese microfinance for farmers. Filomat 32(5), 1513–1521 (2018)
Andriyanov, N.A., Tashlinsky, A.G., Dementiev, V.E.: Detailed clustering based on Gaussian mixture models. Adv. Intell. Syst. Comput. 1251, 437–448 (2021)
Filin, Ya. A., Lependin, A.A.: Application of a Gaussian mixture model for verifying a speaker using arbitrary speech and countering spoofing attacks. Multicore processors, parallel programming, FPGAs. Signal Process. Syst. 6, 64–66 (2016)
Andriyanov, N., Sonin, V.: The use of random process models and machine learning to an-alyze the operation of a taxi order service. ITM Web Conf. 30, 04014 (2019)
Avdeenko, T., Khateev, O.: Taxi service pricing based on online machine learning. Data Mining Big Data 1071, 289–299 (2019)
Krasheninnikov, V.R., Vasil’ev, K.K.: Multidimensional image models and processing. In: Favorskaya, M.N., Jain, L.C. (eds.) Computer Vision in Control Systems-3, ISRL 135, 11–64. Springer International Publishing, Switzerland AG (2018)
Kuvayskova, Y., Klyachkin, V., Krasheninnikov, V.: Recognition and forecasting of a technical object state based on its operation indicators monitoring results. In: 2020 International Multi-conference on Industrial Engineering and Modern Technologies, FarEastCon 2020, pp. 1–6 (2020)
Naseer, S., Liu, W., Sarkar, N.I., Shafiq, M., Choi, J.-G.: Smart city taxi trajectory coverage and capacity evaluation model for vehicular sensor networks. Sustainability 13, 10907 (2021)
Hassouna, F.M.A., Assad, M.: Towards a sustainable public transportation: replacing the conventional taxis by a hybrid taxi fleet in the west bank. Palestine. Int. J. Environ. Res. Public Health 17, 8940 (2020)
Lee, S., Kim, J.H., Park, J., Oh, C., Lee, G.: Deep-learning-based prediction of high-risk taxi drivers using wellness data. Int. J. Environ. Res. Public Health 17, 9505 (2020)
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This work was funded by the Russian Foundation for Basic Research under RFBR grant â„– 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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DOI: https://doi.org/10.1007/978-981-19-3444-5_16
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