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Application of ensemble learning in evaluating the effectiveness of military training

Published: 17 April 2024 Publication History

Abstract

In order to solve the problem of subjective adjustment of evaluation index weights in the Analytic Hierarchy Process (AHP) for military training effectiveness evaluation, and the inability to accumulate historical evaluation experience, which leads to unscientific comparison of effectiveness evaluation results between different units, this study proposes an integrated learning model applied in the field of military training effectiveness evaluation. This model is based on the existing Analytic Hierarchy Process and constructs an indicator system based on historical evaluation data of each unit, Train multiple sub models separately, and through the designed model fusion device, fuse the multiple sub models into the military training effectiveness evaluation model proposed in this paper. Using a certain training dataset as the data source, the samples were divided into training and testing sets in a ratio of 8:2. After training, the proposed model achieved an accuracy of 98.98% in the testing set, with an average absolute error of 1.08%. This model effectively avoids subjective evaluation and can provide scientifically comparable evaluation results.

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    EITCE '23: Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
    October 2023
    1809 pages
    ISBN:9798400708305
    DOI:10.1145/3650400
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 17 April 2024

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