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首页> 外文期刊>Journal of Intelligent Information Systems >Message classification as a basis for studying command and control communications-an evaluation of machine learning approaches
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Message classification as a basis for studying command and control communications-an evaluation of machine learning approaches

机译：消息分类作为学习命令和控制通信的基础-机器学习方法的评估

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摘要

In military command and control, success relies on being able to perform key functions such as communicating intent. Most staff functions are carried out using standard means of text communication. Exactly how members of staff perform their duties, who they communicate with and how, and how they could perform better, is an area of active research. In command and control research, there is not yet a single model which explains all actions undertaken by members of staff well enough to prescribe a set of procedures for how to perform functions in command and control. In this context, we have studied whether automated classification approaches can be applied to textual communication to assist researchers who study command teams and analyze their actions. Specifically, we report the results from evaluating machine leaning with respect to two metrics of classification performance: (1) the precision of finding a known transition between two activities in a work process, and (2) the precision of classifying messages similarly to human researchers that search for critical episodes in a workflow. The results indicate that classification based on text only provides higher precision results with respect to both metrics when compared to other machine learning approaches, and that the precision of classifying messages using text-based classification in already classified datasets was approximately 50%. We present the implications that these results have for the design of support systems based on machine learning, and outline how to practically use text classification for analyzing team communications by demonstrating a specific prototype support tool for workflow analysis.
机译：在军事指挥与控制中，成功取决于能够执行关键功能，例如传达意图。大多数员工功能是使用标准的文本通讯方式执行的。员工的确切工作方式，与谁沟通以及如何以及如何更好地执行工作是积极研究的领域。在指挥与控制研究中，还没有一个模型能够很好地说明人员所采取的所有行动，从而为如何执行指挥与控制功能规定了一套程序。在这种情况下，我们研究了自动分类方法是否可以应用于文本交流，以协助研究指挥小组并分析其行动的研究人员。具体来说，我们报告了根据机器对两个分类性能指标的评估结果：（1）在工作过程中找到两个活动之间已知转换的精度；（2）与人类研究人员相似的消息分类精度搜索工作流中的关键事件。结果表明，与其他机器学习方法相比，基于文本的分类仅在两个指标上都提供了更高的精度结果，并且在已分类的数据集中使用基于文本的分类对消息进行分类的精度约为50％。我们介绍了这些结果对基于机器学习的支持系统设计的影响，并概述了如何通过演示用于工作流分析的特定原型支持工具来实际使用文本分类来分析团队沟通。

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