论文标题
符号知识提取从应用于Lisa Pathfinder收集的宇宙射线数据的不透明预测因子
Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder
论文作者
论文摘要
如今，机器学习模型在空间任务中无处不在，执行了各种各样的任务，从对多元时间序列的预测到输入数据中的特定模式。采用的模型通常是深度神经网络或其他复杂的机器学习算法，提供不透明的预测，即，不允许人类用户理解所提供的预测背后的基本原理。文献中存在几种技术，可以将不透明机器学习模型的令人印象深刻的预测性能与人类无能的预测解释相结合，例如应用符号知识提取程序。在本文中，报告了适用于能够重现lisa Pathfinder空间任务上收集的宇宙射线数据的合奏预测器的不同知识提取器的结果。还提出了关于提取知识的可读性/忠诚度权衡的讨论。
Machine learning models are nowadays ubiquitous in space missions, performing a wide variety of tasks ranging from the prediction of multivariate time series through the detection of specific patterns in the input data. Adopted models are usually deep neural networks or other complex machine learning algorithms providing predictions that are opaque, i.e., human users are not allowed to understand the rationale behind the provided predictions. Several techniques exist in the literature to combine the impressive predictive performance of opaque machine learning models with human-intelligible prediction explanations, as for instance the application of symbolic knowledge extraction procedures. In this paper are reported the results of different knowledge extractors applied to an ensemble predictor capable of reproducing cosmic-ray data gathered on board the LISA Pathfinder space mission. A discussion about the readability/fidelity trade-off of the extracted knowledge is also presented.
