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Journal of System Simulation

Abstract

Abstract: The first problem to be solved in the application of machine learning to the analysis of the replay of the wargaming is the construction of data sets. Due to the standardization requirements of machine learning for data structure, as well as the limitations of computing power and storage, building a machine learning data set through the wargaming data still faces many problems in terms of how to describe the wargaming situation, how to describe the wargaming process, how to handle high dimensional data, and how to prevent data distortion. To solve these problems, this paper constructs a mapping model from the wargaming process data to the machine learning data set, standardizes the mapping process, situation description data range, and data statistics calculation rules of data set construction from the model framework, and designs targeted processing methods from three perspectives of time-related data, geospatial-related data, and high-dimensional data reduction, so as to ensure that the data structure of the data set is unified, and the dimension reduction requirements of highdimensional data and the fidelity requirements of the data set are met. Through the data set construction experiment, it is verified that the data set mapping model constructed in this paper can not only reduce the dimension of high-dimensional data of the wargaming but also prevent the distortion of the constructed data set under the condition of moderate temporal resolution and geospatial resolution.

First Page

608

Last Page

624

CLC

TP391.9

Recommended Citation

Zhang Dayong, Yang Jingyu, Ma Jun, et al. Construction of Machine Learning Data Set for Analyzing the Replay of the Wargaming[J]. Journal of System Simulation, 2024, 36(3): 608-624.

DOI

10.16182/j.issn1004731x.joss.22-1256

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