A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-09-20 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1381921
Ephrem Tibebe Mekonnen, Luca Longo, Pierpaolo Dondio
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Abstract

Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data. On the other hand, adapting these methods to explain deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. This research proposes a novel global post-hoc explainable method for unearthing the key time steps behind the inferences made by deep learning-based time series classifiers. This novel approach generates a decision tree graph, a specific set of rules, that can be seen as explanations, potentially enhancing interpretability. The methodology involves two major phases: (1) training and evaluating deep-learning-based time series classification models, and (2) extracting parameterized primitive events, such as increasing, decreasing, local max and local min, from each instance of the evaluation set and clustering such events to extract prototypical ones. These prototypical primitive events are then used as input to a decision-tree classifier trained to fit the model predictions of the test set rather than the ground truth data. Experiments were conducted on diverse real-world datasets sourced from the UCR archive, employing metrics such as accuracy, fidelity, robustness, number of nodes, and depth of the extracted rules. The findings indicate that this global post-hoc method can improve the global interpretability of complex time series classification models.

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基于时间序列分类器参数化事件原语的全局模型无关规则 XAI 方法。
时间序列分类是一个极具挑战性的研究领域,机器学习和深度学习技术在这一领域表现出色。然而,由于其可解释性极低,这些技术往往被视为黑箱。一方面,有大量可解释人工智能(XAI)方法旨在阐明在图像和表格数据上训练的模型的功能。另一方面,由于时间序列数据的时间性,将这些方法用于解释基于深度学习的时间序列分类器可能并不简单。本研究提出了一种新颖的全局事后可解释方法,用于挖掘基于深度学习的时间序列分类器所做推断背后的关键时间步骤。这种新方法生成的决策树图是一组特定的规则,可被视为解释,潜在地提高了可解释性。该方法包括两个主要阶段:(1)训练和评估基于深度学习的时间序列分类模型;(2)从评估集的每个实例中提取参数化的原始事件,如增加、减少、局部最大和局部最小,并对这些事件进行聚类,以提取原型事件。然后,将这些原型原始事件作为决策树分类器的输入,经过训练,使其符合测试集而非地面实况数据的模型预测。实验在来自 UCR 档案的各种真实世界数据集上进行,采用的指标包括提取规则的准确性、保真度、鲁棒性、节点数和深度。研究结果表明,这种全局事后方法可以提高复杂时间序列分类模型的全局可解释性。
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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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