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Visual exploration of classification models for various data types in risk assessment

机译：可视化探索风险评估中各种数据类型的分类模型

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摘要

In risk assessment applications well-informed decisions need to be made based on large amounts of multidimensional data. In many domains, not only the risk of a wrong decision, but also of the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework to support the decision-making process, which tightly integrates interactive visual exploration with machine learning. The proposed approach uses a series of interactive 2D visualizations of numerical and ordinal data combined with visualization of classification models. These series of visual elements are linked to the classifier's performance, which is visualized using an interactive performance curve. This interaction allows the decision-maker to steer the classification model and instantly identify the critical, cost-changing data elements in the various linked visualizations. The critical data elements are represented as images in order to trigger associations related to the knowledge of the expert. In this way the data visualization and classification results are not only linked together, but are also linked back to the classification model. Such a visual analytics framework allows the user to interactively explore the costs of his decisions for different settings of the model and, accordingly, use the most suitable classification model. More informed and reliable decisions result. A case study in the forensic psychiatry domain reveals the usefulness of the suggested approach.
机译：在风险评估应用中，需要基于大量的多维数据做出明智的决策。在许多领域中，最重要的不仅是错误决策的风险，还包括可能决策成本之间的权衡。在本文中，我们描述了一个支持决策过程的框架，该框架将交互式视觉探索与机器学习紧密集成在一起。所提出的方法使用了一系列的交互式2D数字和顺序数据可视化，以及分类模型的可视化。这些视觉元素系列与分类器的性能相关联，可使用交互式性能曲线将其可视化。这种交互作用使决策者可以控制分类模型，并立即在各种链接的可视化图中识别关键的，不断变化的成本数据元素。关键数据元素表示为图像，以触发与专家知识有关的关联。这样，数据可视化和分类结果不仅链接在一起，而且还链接回分类模型。这种视觉分析框架允许用户针对模型的不同设置交互地探索其决策的成本，并因此使用最合适的分类模型。得出更明智和可靠的决策。法医精神病学领域的案例研究揭示了所建议方法的有用性。

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