VAE-LIME：基于深度生成模型的局部数据驱动模型解释方法
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry
摘要：
应用于生成数据驱动模型的机器学习缺乏透明度，导致过程工程师对依赖模型预测来优化其工业过程失去信心。使用数据驱动模型将行业流程提升到一定程度的自主性尤其具有挑战性，因为这些模型的第一个用户通常是拥有数十年经验的流程专家。有必要向过程工程师展示，不仅是模型预测，而且还有它们的可解释性。为此，文献中提出了几种方法。局部可解释模型不可知解释（LIME）方法最近引起了研究界的极大兴趣。该方法的原理是通过在局部随机生成人工数据点来训练一个局部逼近黑盒模型的线性模型。最近出现了基于LIME的模型不可知局部可解释性解决方案，以改进原始方法。在本文中，我们提出了一种新的方法，VAE-LIME，本地可解释的数据驱动模型预测的高炉生产的铁水温度。这样的炼铁过程数据的特征在于具有高度相关性的多变量时间序列，其代表高炉中的潜在过程。我们的贡献是使用变分自编码器（VAE）从数据中学习复杂的高炉过程特性。VAE旨在生成最佳人工样本以训练局部可解释模型，该局部可解释模型在由黑盒模型处理的输入样本的邻域中更好地表示黑盒模型以进行预测。与LIME相比，VAE-LIME显示出显着改善的局部可解释线性模型的局部保真度与黑盒模型，从而产生鲁棒的模型可解释性。
英文摘要：
Machine learning applied to generate data-driven models are lacking of transparency leading the process engineer to lose confidence in relying on the model predictions to optimize his industrial process. Bringing processes in the industry to a certain level of autonomy using data-driven models is particularly challenging as the first user of those models, is the expert in the process with often decades of experience. It is necessary to expose to the process engineer, not solely the model predictions, but also their interpretability. To that end, several approaches have been proposed in the literature. The Local Interpretable Model-agnostic Explanations (LIME) method has gained a lot of interest from the research community recently. The principle of this method is to train a linear model that is locally approximating the black-box model, by generating randomly artificial data points locally. Model-agnostic local interpretability solutions based on LIME have recently emerged to improve the original method. We present in this paper a novel approach, VAE-LIME, for local interpretability of data-driven models forecasting the temperature of the hot metal produced by a blast furnace. Such ironmaking process data is characterized by multivariate time series with high inter-correlation representing the underlying process in a blast furnace. Our contribution is to use a Variational Autoencoder (VAE) to learn the complex blast furnace process characteristics from the data. The VAE is aiming at generating optimal artificial samples to train a local interpretable model better representing the black-box model in the neighborhood of the input sample processed by the black-box model to make a prediction. In comparison with LIME, VAE-LIME is showing a significantly improved local fidelity of the local interpretable linear model with the black-box model resulting in robust model interpretability.
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