论文标题
具有复发性神经网络的预测类中的值级特征归因的视觉摘要
Visual Summary of Value-level Feature Attribution in Prediction Classes with Recurrent Neural Networks
论文作者
论文摘要
深层复发性神经网络（RNN）越来越多地用于使用时间序列的决策。但是，了解RNN模型如何产生最终预测仍然是一个重大挑战。对序列预测的解释RNN模型的现有工作通常集中在解释个人数据实例（例如患者或学生）的预测。由于最先进的预测模型是在数百万个实例中优化了数百万个参数，因此解释单个数据实例的预测很容易错过更大的图像。此外，许多胜过RNN模型都使用多热编码来表示特征值属性的可解释性的存在/不存在。我们提出了Visfa，这是一个交互式系统，可在视觉上汇总随着时间的推移特征归因的不同特征值。 Visfa量表大量数据，例如包含120万个高维时间事件的电子健康记录的模拟数据集。我们证明，Visfa可以帮助我们推理RNN预测，并通过将复杂的归因提炼成紧凑且易于解释的可视化，从数据中发现洞察力。
Deep Recurrent Neural Networks (RNN) is increasingly used in decision-making with temporal sequences. However, understanding how RNN models produce final predictions remains a major challenge. Existing work on interpreting RNN models for sequence predictions often focuses on explaining predictions for individual data instances (e.g., patients or students). Because state-of-the-art predictive models are formed with millions of parameters optimized over millions of instances, explaining predictions for single data instances can easily miss a bigger picture. Besides, many outperforming RNN models use multi-hot encoding to represent the presence/absence of features, where the interpretability of feature value attribution is missing. We present ViSFA, an interactive system that visually summarizes feature attribution over time for different feature values. ViSFA scales to large data such as the MIMIC dataset containing the electronic health records of 1.2 million high-dimensional temporal events. We demonstrate that ViSFA can help us reason RNN prediction and uncover insights from data by distilling complex attribution into compact and easy-to-interpret visualizations.
