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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Quantifying the Relative Importance of Variables and Groups of Variables in Remote Sensing Classifiers Using Shapley Values and Game Theory
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Quantifying the Relative Importance of Variables and Groups of Variables in Remote Sensing Classifiers Using Shapley Values and Game Theory

机译：使用福利值和博弈论量化遥感分类器中变量和变量组的相对重要性

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摘要

Remote sensing image classification applications often involve determining which variables are the most important to obtain the best accuracy. Common metrics for assessing variable importance such as a mean decrease in accuracy (MDA) typically provide values in scaled units that are difficult to interpret, and do not easily accommodate user-defined groups of variables. In this letter, an improved method of quantifying the importance of classifier variables is developed and demonstrated in the context of land-cover classification using the random forest algorithm. The proposed method employs concepts from game theory and relies on a metric known as the Shapley value, which allows the importance of variables to be easily interpreted by providing a quantitative assessment of each variable's contribution to classifier accuracy. Moreover, unlike MDA, the method also applies to arbitrary, user-defined groups of variables. The approach described herein thus provides a robust alternative to single-variable selection using MDA and can be used with any type of classifier.
机译：遥感图像分类应用程序通常涉及确定哪些变量是获得最佳精度最重要的。用于评估变量重要性的常见度量，例如准确度（MDA）的平均降低（MDA）通常在难以解释的缩放单元中提供值，并且不容易容纳用户定义的变量组。在这封信中，在使用随机林算法的陆地覆盖分类的背景下，开发和演示了一种改进的量化分类器变量的重要性方法。该方法采用了博弈论的概念，并依赖于称为福芙值的度量，这允许通过提供对每个变量对分类器精度的贡献的定量评估来容易地解释变量的重要性。此外，与MDA不同，该方法还适用于任意，用户定义的变量组。因此，本文描述的方法提供了使用MDA的单变量选择的稳健替代，并且可以与任何类型的分类器一起使用。

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