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Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing. 为临床自然语言处理中的半监督学习自动数据标注中的分歧建模。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-02 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1374162
Hongshu Liu, Nabeel Seedat, Julia Ive

Introduction: Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision-making in healthcare contexts. This is especially true since many state-of-the-art systems are trained using the data which have been labeled automatically (self-supervised mode) and tend to overfit.

Methods: In this study, we investigate the quality of uncertainty estimates from a range of current state-of-the-art predictive models applied to the problem of observation detection in radiology reports. This problem remains understudied for Natural Language Processing in the healthcare domain.

Results: We demonstrate that Gaussian Processes (GPs) provide superior performance in quantifying the risks of three uncertainty labels based on the negative log predictive probability (NLPP) evaluation metric and mean maximum predicted confidence levels (MMPCL), whilst retaining strong predictive performance.

Discussion: Our conclusions highlight the utility of probabilistic models applied to "noisy" labels and that similar methods could provide utility for Natural Language Processing (NLP) based automated labeling tasks.

导言:提供不确定性准确估计值的计算模型对于医疗决策相关的风险管理至关重要。由于许多最先进的系统都是使用自动标注的数据(自我监督模式)进行训练的,因此往往会出现过拟合的情况,这一点尤为重要:在本研究中,我们将一系列当前最先进的预测模型应用于放射学报告中的观察结果检测问题,对其不确定性估计的质量进行了调查。这一问题在医疗保健领域的自然语言处理中仍未得到充分研究:结果:我们证明了高斯过程(GPs)在量化基于负对数预测概率(NLPP)评估指标和平均最大预测置信水平(MMPCL)的三种不确定性标签的风险方面具有卓越的性能,同时保持了强大的预测性能:我们的结论强调了应用于 "噪声 "标签的概率模型的实用性,类似的方法可为基于自然语言处理(NLP)的自动标签任务提供实用性。
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引用次数: 0
Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking. 提高基于症状的健康检查器的诊断准确性:利用临床案例和基准的综合机器学习方法。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1397388
Leila Aissaoui Ferhi, Manel Ben Amar, Fethi Choubani, Ridha Bouallegue

Introduction: The development of machine learning models for symptom-based health checkers is a rapidly evolving area with significant implications for healthcare. Accurate and efficient diagnostic tools can enhance patient outcomes and optimize healthcare resources. This study focuses on evaluating and optimizing machine learning models using a dataset of 10 diseases and 9,572 samples.

Methods: The dataset was divided into training and testing sets to facilitate model training and evaluation. The following models were selected and optimized: Decision Tree, Random Forest, Naive Bayes, Logistic Regression and K-Nearest Neighbors. Evaluation metrics included accuracy, F1 scores, and 10-fold cross-validation. ROC-AUC and precision-recall curves were also utilized to assess model performance, particularly in scenarios with imbalanced datasets. Clinical vignettes were employed to gauge the real-world applicability of the models.

Results: The performance of the models was evaluated using accuracy, F1 scores, and 10-fold cross-validation. The use of ROC-AUC curves revealed that model performance improved with increasing complexity. Precision-recall curves were particularly useful in evaluating model sensitivity in imbalanced dataset scenarios. Clinical vignettes demonstrated the robustness of the models in providing accurate diagnoses.

Discussion: The study underscores the importance of comprehensive model evaluation techniques. The use of clinical vignette testing and analysis of ROC-AUC and precision-recall curves are crucial in ensuring the reliability and sensitivity of symptom-based health checkers. These techniques provide a more nuanced understanding of model performance and highlight areas for further improvement.

Conclusion: This study highlights the significance of employing diverse evaluation metrics and methods to ensure the robustness and accuracy of machine learning models in symptom-based health checkers. The integration of clinical vignettes and the analysis of ROC-AUC and precision-recall curves are essential steps in developing reliable and sensitive diagnostic tools.

简介为基于症状的健康检查器开发机器学习模型是一个快速发展的领域,对医疗保健具有重大意义。准确高效的诊断工具可以提高患者的治疗效果,优化医疗资源。本研究的重点是使用包含 10 种疾病和 9,572 个样本的数据集评估和优化机器学习模型:方法:将数据集分为训练集和测试集,以便于模型的训练和评估。选择并优化了以下模型:决策树、随机森林、奈夫贝叶斯、逻辑回归和 K-近邻。评估指标包括准确率、F1 分数和 10 倍交叉验证。此外,还利用 ROC-AUC 和精度-召回曲线来评估模型性能,尤其是在数据集不平衡的情况下。此外,还采用了临床案例来衡量模型在现实世界中的适用性:结果:使用准确率、F1 分数和 10 倍交叉验证评估了模型的性能。使用 ROC-AUC 曲线显示,模型性能随着复杂度的增加而提高。精确度-召回曲线对评估不平衡数据集情况下的模型灵敏度特别有用。临床案例证明了模型在提供准确诊断方面的稳健性:本研究强调了综合模型评估技术的重要性。讨论:该研究强调了综合模型评估技术的重要性,使用临床小样本测试以及 ROC-AUC 和精确度-召回曲线分析对于确保基于症状的健康检查器的可靠性和灵敏度至关重要。这些技术能更细致地了解模型的性能,并突出需要进一步改进的地方:本研究强调了采用不同的评估指标和方法来确保基于症状的健康检查器中机器学习模型的稳健性和准确性的重要性。在开发可靠、灵敏的诊断工具时,整合临床案例、分析 ROC-AUC 和精确度-召回曲线是必不可少的步骤。
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引用次数: 0
The application of explainable artificial intelligence methods to models for automatic creativity assessment. 将可解释人工智能方法应用于创造力自动评估模型。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1310518
Anastasia S Panfilova, Ekaterina A Valueva, Ivan Y Ilyin

Objective: The study is devoted to comparing various models based on Artificial Intelligence to determine the level of creativity based on drawings performed using the Urban test, as well as analyzing the results of applying explainable artificial intelligence methods to a trained model to identify the most relevant features in drawings that influence the model's prediction.

Methods: The dataset is represented by a set of 1,823 scanned forms of drawings of participants performed according to the Urban test. The test results of each participant were assessed by an expert. Preprocessed images were used for fine-tuning pre-trained models such as MobileNet, ResNet18, AlexNet, DenseNet, ResNext, EfficientNet, ViT with additional linear layers to predict the participant's score. Visualization of the areas that are of greatest importance from the point of view of the model was carried out using the Gradient-weighted Class Activation Mapping (Grad-CAM) method.

Results: Trained models based on MobileNet showed the highest prediction accuracy rate of 76%. The results of the application of explainable artificial intelligence demonstrated areas of interest that correlated with the criteria for expert assessment according to the Urban test. Analysis of erroneous predictions of the model in terms of interpretation of areas of interest made it possible to clarify the features of the drawing on which the model relies, contrary to the expert.

Conclusion: The study demonstrated the possibility of using neural network methods for automated diagnosis of the level of creativity according to the Urban test based on the respondents' drawings. The application of explainable artificial intelligence methods to the trained model demonstrated the compliance of the identified activation zones with the rules of expert assessment according to the Urban test.

研究目的本研究致力于比较各种基于人工智能的模型,以确定使用城市测试进行的绘画的创造力水平,并分析对训练有素的模型应用可解释人工智能方法的结果,以确定绘画中影响模型预测的最相关特征:该数据集由一组 1823 张参与者根据城市测试绘制的图画扫描表组成。每位参与者的测试结果都由一位专家进行评估。预处理后的图像用于微调预训练模型,如 MobileNet、ResNet18、AlexNet、DenseNet、ResNext、EfficientNet、ViT,并增加线性层以预测参与者的得分。使用梯度加权类激活映射(Grad-CAM)方法对模型最重要的区域进行了可视化:基于 MobileNet 的训练模型显示出最高的预测准确率,达到 76%。可解释人工智能的应用结果表明,根据城市测试,感兴趣的领域与专家评估标准相关。通过对感兴趣领域的解释对模型的错误预测进行分析,可以澄清模型所依赖的图纸特征,这与专家的预测相反:这项研究表明,根据基于受访者绘画作品的城市测试,使用神经网络方法对创造力水平进行自动诊断是可行的。将可解释的人工智能方法应用于训练好的模型,证明了根据城市测试确定的激活区符合专家评估规则。
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引用次数: 0
Efficient incremental training using a novel NMT-SMT hybrid framework for translation of low-resource languages. 使用新型 NMT-SMT 混合框架对低资源语言翻译进行高效增量训练。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-25 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1381290
Kumar Bhuvaneswari, Murugesan Varalakshmi

The data-hungry statistical machine translation (SMT) and neural machine translation (NMT) models offer state-of-the-art results for languages with abundant data resources. However, extensive research is imperative to make these models perform equally well for low-resource languages. This paper proposes a novel approach to integrate the best features of the NMT and SMT systems for improved translation performance of low-resource English-Tamil language pair. The suboptimal NMT model trained with the small parallel corpus translates the monolingual corpus and selects only the best translations, to retrain itself in the next iteration. The proposed method employs the SMT phrase-pair table to determine the best translations, based on the maximum match between the words of the phrase-pair dictionary and each of the individual translations. This repeating cycle of translation and retraining generates a large quasi-parallel corpus, thus making the NMT model more powerful. SMT-integrated incremental training demonstrates a substantial difference in translation performance as compared to the existing approaches for incremental training. The model is strengthened further by adopting a beam search decoding strategy to produce k best possible translations for each input sentence. Empirical findings prove that the proposed model with BLEU scores of 19.56 and 23.49 outperforms the baseline NMT with scores 11.06 and 17.06 for Eng-to-Tam and Tam-to-Eng translations, respectively. METEOR score evaluation further corroborates these results, proving the supremacy of the proposed model.

对数据要求极高的统计机器翻译(SMT)和神经机器翻译(NMT)模型可为数据资源丰富的语言提供最先进的结果。然而,要使这些模型在低资源语言中同样表现出色,广泛的研究势在必行。本文提出了一种整合 NMT 和 SMT 系统最佳功能的新方法,以提高低资源英语-泰米尔语对的翻译性能。使用小型平行语料库训练的次优 NMT 模型翻译单语语料库,并只选择最佳翻译,以便在下一次迭代中重新训练自己。建议的方法采用 SMT 短语对表,根据短语对词典中的单词与每个单个译文之间的最大匹配度来确定最佳译文。这种重复的翻译和再训练循环会产生一个大型准平行语料库,从而使 NMT 模型更加强大。与现有的增量训练方法相比,集成 SMT 的增量训练在翻译性能上有很大的不同。通过采用波束搜索解码策略为每个输入句子生成 k 个最佳译文,该模型得到了进一步加强。实证结果证明,在英译潭和潭译英的翻译中,拟议模型的 BLEU 得分分别为 19.56 和 23.49,优于基准 NMT 的 11.06 和 17.06。METEOR 分数评估进一步证实了这些结果,证明了所提出模型的优越性。
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引用次数: 0
Algorithmic management and human-centered task design: a conceptual synthesis from the perspective of action regulation and sociomaterial systems theory. 算法管理和以人为本的任务设计:从行动调节和社会物质系统理论的角度进行概念综合。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-25 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1441497
Carsten Röttgen, Britta Herbig, Tobias Weinmann, Andreas Müller

This paper aims to explain potential psychological effects of algorithmic management (AM) on human-centered task design and with that also workers' mental well-being. For this, we link research on algorithmic management (AM) with Sociomaterial System Theory and Action Regulation Theory (ART). Our main assumption is that psychological effects of sociomaterial systems, such as AM, can be explained by their impact on human action. From the synthesis of the theories, mixed effects on human-centered task design can be derived: It can be expected that AM contributes to fewer action regulation opportunities (i.e., job resources like job autonomy, transparency, predictability), and to lower intellectual demands (i.e., challenge demands like task complexity, problem solving). Moreover, it can be concluded that AM is related with more regulation problems (i.e., hindrance demands like overtaxing regulations) but also fewer regulation problems (like regulation obstacles, uncertainty). Based on these considerations and in line with the majority of current research, it can be assumed that the use of AM is indirectly associated with higher risks to workers' mental well-being. However, we also identify potential positive effects of AM as some stressful and demotivating obstacles at work are often mitigated. Based on these considerations, the main question of future research is not whether AM is good or bad for workers, but rather how work under AM can be designed to be humane. Our proposed model can guide and support researchers and practitioners in improving the understanding of the next generation of AM systems.

本文旨在解释算法管理(AM)对以人为本的任务设计以及工人心理健康的潜在心理影响。为此,我们将算法管理(AM)研究与社会物质系统理论和行动调节理论(ART)联系起来。我们的主要假设是,社会物质系统(如 AM)的心理效应可以通过其对人类行动的影响来解释。综合这些理论,可以得出以人为中心的任务设计的混合效应:可以预计,AM 会减少行动调节机会(即工作资源,如工作自主性、透明度、可预测性),降低智力要求(即挑战要求,如任务复杂性、问题解决)。此外,还可以得出这样的结论:AM 与更多的监管问题(即监管过度等阻碍性需求)有关,但也与较少的监管问题(如监管障碍、不确定性)有关。基于这些考虑,并与目前的大多数研究相一致,我们可以认为,AM 的使用与工人精神健康的高风险间接相关。不过,我们也发现了调幅装置的潜在积极影响,因为工作中的一些压力和挫伤积极性的障碍往往会得到缓解。基于这些考虑,未来研究的主要问题不是调幅技术对工人是好是坏,而是如何设计调幅技术下的工作才能人性化。我们提出的模型可以为研究人员和从业人员提供指导和支持,帮助他们更好地理解下一代人工智能系统。
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引用次数: 0
Anomaly detection via Gumbel Noise Score Matching. 通过 Gumbel Noise Score Matching 进行异常检测。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-24 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1441205
Ahsan Mahmood, Junier Oliva, Martin Andreas Styner

We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e., the gradients of log likelihoods w.r.t. inputs, of continuously relaxed categorical distributions. We test our method on a suite of anomaly detection tabular datasets. GNSM achieves a consistently high performance across all experiments. We further demonstrate the flexibility of GNSM by applying it to image data where the model is tasked to detect poor segmentation predictions. Images ranked anomalous by GNSM show clear segmentation failures, with the anomaly scores strongly correlating with segmentation metrics computed on ground-truth. We outline the score matching training objective utilized by GNSM and provide an open-source implementation of our work.

我们提出的 Gumbel Noise Score Matching(GNSM)是一种新型的无监督方法,用于检测分类数据中的异常情况。GNSM 通过估算连续松弛分类分布的分数(即输入时的对数似然梯度)来实现这一目标。我们在一套异常检测表格数据集上测试了我们的方法。在所有实验中,GNSM 始终保持着较高的性能。通过将 GNSM 应用于图像数据,我们进一步证明了 GNSM 的灵活性。被 GNSM 评为异常的图像显示出明显的分割失败,异常分数与根据地面实况计算的分割指标密切相关。我们概述了 GNSM 使用的分数匹配训练目标,并提供了我们工作的开源实现。
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引用次数: 0
Artificial intelligence and machine learning applications for cultured meat. 人工智能和机器学习在养殖肉类中的应用。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-24 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1424012
Michael E Todhunter, Sheikh Jubair, Ruchika Verma, Rikard Saqe, Kevin Shen, Breanna Duffy

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

养殖肉类有可能成为肉类产业的补充,减少对环境、道德和健康的影响。然而,重大的技术挑战依然存在,需要时间和资源密集型的研发工作。机器学习有可能通过简化实验、预测最佳结果以及减少实验时间和资源来加速养殖肉类技术的发展。然而,机器学习在养殖肉类中的应用还处于起步阶段。本综述涵盖了迄今为止机器学习在肉类养殖中的应用,并探讨了未来的可能性。我们讨论了培养肉研究与开发的四个主要领域:建立细胞系、细胞培养基设计、显微镜和图像分析以及生物加工和食品加工优化。此外,我们还对与中药研究相关的数据集进行了调查。本综述旨在为培养肉和机器学习科学家提供必要的基础,以确定培养肉和机器学习交叉领域的研究机会。
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引用次数: 0
Towards enhanced creativity in fashion: integrating generative models with hybrid intelligence. 增强时尚创意:将生成模型与混合智能相结合。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-23 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1460217
Alexander Ryjov, Vagan Kazaryan, Andrey Golub, Alina Egorova

Introduction: This study explores the role and potential of large language models (LLMs) and generative intelligence in the fashion industry. These technologies are reshaping traditional methods of design, production, and retail, leading to innovation, product personalization, and enhanced customer interaction.

Methods: Our research analyzes the current applications and limitations of LLMs in fashion, identifying challenges such as the need for better spatial understanding and design detail processing. We propose a hybrid intelligence approach to address these issues.

Results: We find that while LLMs offer significant potential, their integration into fashion workflows requires improvements in understanding spatial parameters and creating tools for iterative design.

Discussion: Future research should focus on overcoming these limitations and developing hybrid intelligence solutions to maximize the potential of LLMs in the fashion industry.

简介本研究探讨了大型语言模型(LLM)和生成智能在时尚产业中的作用和潜力。这些技术正在重塑传统的设计、生产和零售方法,带来创新、产品个性化和增强的客户互动:我们的研究分析了当前 LLM 在时尚领域的应用和局限性,发现了一些挑战,如需要更好的空间理解和设计细节处理。我们提出了一种混合智能方法来解决这些问题:结果:我们发现,虽然 LLMs 具有巨大的潜力,但将其整合到时尚工作流程中需要改进对空间参数的理解,并创建用于迭代设计的工具:讨论:未来的研究应侧重于克服这些局限性和开发混合智能解决方案,以最大限度地发挥 LLM 在时尚产业中的潜力。
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引用次数: 0
Image restoration in frequency space using complex-valued CNNs. 使用复值 CNN 在频率空间中修复图像。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-23 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1353873
Zafran Hussain Shah, Marcel Müller, Wolfgang Hübner, Henning Ortkrass, Barbara Hammer, Thomas Huser, Wolfram Schenck

Real-valued convolutional neural networks (RV-CNNs) in the spatial domain have outperformed classical approaches in many image restoration tasks such as image denoising and super-resolution. Fourier analysis of the results produced by these spatial domain models reveals the limitations of these models in properly processing the full frequency spectrum. This lack of complete spectral information can result in missing textural and structural elements. To address this limitation, we explore the potential of complex-valued convolutional neural networks (CV-CNNs) for image restoration tasks. CV-CNNs have shown remarkable performance in tasks such as image classification and segmentation. However, CV-CNNs for image restoration problems in the frequency domain have not been fully investigated to address the aforementioned issues. Here, we propose several novel CV-CNN-based models equipped with complex-valued attention gates for image denoising and super-resolution in the frequency domains. We also show that our CV-CNN-based models outperform their real-valued counterparts for denoising super-resolution structured illumination microscopy (SR-SIM) and conventional image datasets. Furthermore, the experimental results show that our proposed CV-CNN-based models preserve the frequency spectrum better than their real-valued counterparts in the denoising task. Based on these findings, we conclude that CV-CNN-based methods provide a plausible and beneficial deep learning approach for image restoration in the frequency domain.

空间域实值卷积神经网络(RV-CNN)在许多图像复原任务(如图像去噪和超分辨率)中的表现都优于传统方法。对这些空间域模型产生的结果进行傅立叶分析,可以发现这些模型在正确处理全频谱方面存在局限性。缺乏完整的频谱信息会导致纹理和结构元素的缺失。为了解决这一局限性,我们探索了复值卷积神经网络(CV-CNN)在图像复原任务中的潜力。复值卷积神经网络在图像分类和分割等任务中表现出色。然而,针对频域图像复原问题的 CV-CNN 还没有得到充分研究以解决上述问题。在此,我们提出了几种基于 CV-CNN 的新型模型,这些模型配备了复值注意门,可用于频域中的图像去噪和超分辨率。在对超分辨率结构照明显微镜(SR-SIM)和传统图像数据集进行去噪时,我们的基于 CV-CNN 的模型优于其对应的实值模型。此外,实验结果表明,在去噪任务中,我们提出的基于 CV-CNN 的模型比其对应的实值模型能更好地保留频谱。基于这些发现,我们得出结论:基于 CV-CNN 的方法为频域图像复原提供了一种可行且有益的深度学习方法。
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引用次数: 0
A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers. 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。
IF 3 Q2 COMPUTER SCIENCE, 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

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.

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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引用次数: 0
期刊
Frontiers in Artificial Intelligence
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