论文标题
通过Dataview提取和阴影模型创建黑框计算机学习模型的解释性
Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation
论文作者
论文摘要
使用大量数据训练的深度学习模型倾向于捕获数据的一种观点及其相关的映射。建立在相同培训数据上的不同深度学习模型可能会根据所使用的潜在技术捕获数据的不同观点。为了解释BlackBox深度学习模型所产生的决策，我们认为必须忠实地重现该模型对培训数据的看法。然后可以将这种忠实的繁殖用于解释。我们研究了两种用于数据视图提取的方法：爬山方法和一种以GAN为导向的方法。然后，我们使用此综合数据来创建用于解释生成的阴影模型：决策-Tree模型和正式概念分析模型。我们在公共数据集训练的黑框模型上评估了这些方法，并在解释生成中显示了其有用性。
Deep learning models trained using massive amounts of data tend to capture one view of the data and its associated mapping. Different deep learning models built on the same training data may capture different views of the data based on the underlying techniques used. For explaining the decisions arrived by blackbox deep learning models, we argue that it is essential to reproduce that model's view of the training data faithfully. This faithful reproduction can then be used for explanation generation. We investigate two methods for data view extraction: hill-climbing approach and a GAN-driven approach. We then use this synthesized data for creating shadow models for explanation generation: Decision-Tree model and Formal Concept Analysis based model. We evaluate these approaches on a Blackbox model trained on public datasets and show its usefulness in explanation generation.
