Users need to understand the predictions of a classifier, especially when decisions based on the predictions can have severe consequences. The explanation of a prediction reveals the reason why a classifier makes a certain prediction, and it helps users to accept or reject the prediction with greater confidence. This paper proposes an explanation method called Pattern Aided Local Explanation (PALEX) to provide instance-level explanations for any classifier. PALEX takes a classifier, a test instance and a frequent pattern set summarizing the training data of the classifier as inputs, and then outputs the supporting evidence that the classifier considers important for the prediction of the instance. To study the local behavior of a classifier in the vicinity of the test instance, PALEX uses the frequent pattern set from the training data as an extra input to guide generation of new synthetic samples in the vicinity of the test instance. Contrast patterns are also used in PALEX to identify locally discriminative features in the vicinity of a test instance. PALEX is particularly effective for scenarios where there exist multiple explanations. In our experiments, we compare PALEX to several state-of-the-art explanation methods over a range of benchmark datasets and find that it can identify explanations with both high precision and high recall.

中文翻译：

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利用模式来解释个人预测

用户需要了解分类器的预测，尤其是当基于预测的决策可能产生严重后果时。对预测的解释揭示了分类器做出特定预测的原因，它可以帮助用户更加自信地接受或拒绝预测。本文提出了一种称为模式辅助局部说明（PALEX）的说明方法，以为任何分类器提供实例级说明。PALEX将分类器，测试实例和将分类器的训练数据汇总的频繁模式集作为输入，然后输出分类器认为对实例的预测很重要的支持证据。要研究测试实例附近的分类器的局部行为，PALEX使用来自训练数据的频繁模式集作为额外输入，以指导在测试实例附近生成新的合成样本。对比模式还用于PALEX中，以识别测试实例附近的局部区分特征。PALEX对于存在多种解释的方案特别有效。在我们的实验中，我们将PALEX与多种基准数据集上的几种最新解释方法进行了比较，发现它可以识别高精度和高召回率的解释。PALEX对于存在多种解释的方案特别有效。在我们的实验中，我们将PALEX与多种基准数据集上的几种最新解释方法进行了比较，发现它可以识别高精度和高召回率的解释。PALEX对于存在多种解释的方案特别有效。在我们的实验中，我们将PALEX与多种基准数据集上的几种最新解释方法进行了比较，发现它可以识别高精度和高查全率的解释。