Lidé na fakultě
Tetiana Reznychenko

2024, 2024 Smart City Symposium Prague (SCSP), New York, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC), p. 1-5), ISBN 979-8-3503-6095-0, ISSN 2691-3666

The paper deals with a comparative analysis of three widely used data analysis methods: logistic regression, random forest, and neural networks. These methods have been evaluated in terms of accuracy, and computational efficiency and applied to different types of data sets, including both simulated and real MaaS data. The study aims to compare the efficiency of each method in classification tasks. The study leads to specific recommendations on which method to use under various circumstances, contributing to the decision-making process in data analysis projects. We have shown that random forests generally provide better accuracy and are resistant to over-training. Neural networks can achieve comparable performance under certain conditions, although at a high computational cost. Logistic regression shows limitations in dealing with complex data structures.

Stať ve sborníku z mezinár. konf.

2023, 2023 Smart City Symposium Prague, New York, IEEE Press), p. 1-6), ISBN 979-8-3503-2162-3, ISSN 2691-3666

The paper deals with the analysis of histograms of discrete data collected in questionnaires obtained for individual realizations of the target variable. The main aim of the analysis isto explore the influence of combinations of explanatory variables, represented by responses to the questionnaire, on the behavior of the target variable of the questionnaire. In this paper, an automated approach to histogram comparison is proposed based on coding combinations of data and detecting significant differences in frequencies using the Marascuilo procedure. This is the main contribution of the paper. The approach is validated using a simulated questionnaire in which respondents answered regarding their intention to purchase an electric vehicle subject to finance, leasing, and charging availability, as well as their driving style. The results of the experiments are demonstrated.

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2023, Proceedings of the The 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) IDAACS’2023, Dortmund, IEEE), p. 51-56), ISBN 979-8-3503-5804-9, ISSN 2770-4254

The paper considers the problem of online prediction of a count variable based on real-time explanatory data of mixed count and categorical nature. The presented solution is based on (i) recursive Bayesian estimation of a mixture model of Poisson-distributed explanatory counts, using the categorical explanatory variable as a measurable pointer of the mixture, (ii) construction of a mixture of local Poisson regressions on the clustered data, and (iii) use of the pre-estimated mixtures for online prediction of the target count using actual measured explanatory data. The latter is one of the main contributions of the proposed approach. In addition, the dynamic model of the categorical explanatory variable preserves the functionality of the algorithm in case of its measurement failure. The experiments with simulations and real data report lower prediction errors compared to theoretical counterparts.

Stať ve sborníku z mezinár. konf.