Besides accuracy, recent studies on machine learning models have been addressing the question on how the obtained results can be interpreted. Indeed, while complex machine learning models are able to provide very good results in terms of accuracy even in challenging applications, it is difficult to interpret them. Aiming at providing some interpretability for such models, one of the most famous methods, called SHAP, borrows the Shapley value concept from game theory in order to locally explain the predicted outcome of an instance of interest. As the SHAP values calculation needs previous computations on all possible coalitions of attributes, its computational cost can be very high. Therefore, a SHAP-based method called Kernel SHAP adopts an efficient strategy that approximate such values with less computational effort. In this paper, we also address local interpretability in machine learning based on Shapley values. Firstly, we provide a straightforward formulation of a SHAP-based method for local interpretability by using the Choquet integral, which leads to both Shapley values and Shapley interaction indices. Moreover, we also adopt the concept of $k$-additive games from game theory, which contributes to reduce the computational effort when estimating the SHAP values. The obtained results attest that our proposal needs less computations on coalitions of attributes to approximate the SHAP values.
Neuro-inspired edge feature fusion using Choquet integrals
Universidad Publica de Navarra（纳瓦拉公立大学）；Institute of Smart Cities, Universidad Publica de Navarra（纳瓦拉公立大学智慧城市研究所）；NavarraBiomed, Complejo Hospitalario de Navarra（纳瓦拉生物医学研究所（纳瓦拉医院综合））；Universidade Federal do Rio Grande（联邦里奥格兰德大学）；Centro de Ciências Computacionais, Universidade Federal do Rio Grande（联邦里奥格兰德大学计算机科学中心）；Dpto. Estadistica, Informatica y Matematicas, Universidad Publica de Navarra（纳瓦拉公立大学统计、信息与数学系）；Programa de Pós-Graduação em Modelagem Computacional, Universidade Federal do Rio Grande（联邦里奥格兰德大学计算建模研究生项目）
Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Data
School of Control Science（控制科学学院）；Engineering Shandong University Jinan, Shandong（山东大学工程学院济南山东）；Shandong Inspur Science Research Institute Co., Ltd. Jinan, Shandong（山东 Inspur 科研院有限公司济南山东）
Clinical Interpretability of Deep Learning Segmentation Through Shapley-Derived Agreement and Uncertainty Metrics
Department of Mechanical Engineering, University of Washington（华盛顿大学机械工程系）；University of Washington School of Medicine（华盛顿大学医学院）；Department of Neurological Surgery, University of Washington（华盛顿大学神经外科系）
A review on development of eco-friendly filters in Nepal for use in cigarettes and masks and Air Pollution Analysis with Machine Learning and SHAP Interpretability
Institute of Engineering, Pulchowk Campus, Tribhuvan University（特里布万大学普尔乔克校区工程学院）；Nepal Engineering College, Pokhara University（博卡拉大学尼泊尔工程学院）
A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
Center for Energy and Geo Processing (CeGP)（能源与地球处理中心）；Omni Lab for Intelligent Visual Engineering and Science (OLIVES)（智能视觉工程与科学实验室）；School of Electrical and Computer Engineering（电气与计算机工程学院）；Georgia Institute of Technology（佐治亚理工学院）
Enhancing ML Models Interpretability for Credit Scoring
Dept. of Computer Science, Delft University of Technology（代尔夫特理工大学计算机科学系）；Dept. of Applied Mathematics, Delft University of Technology（代尔夫特理工大学应用数学系）；FF Quant Advisory B.V.（FF量化咨询公司）
Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability
Department of Economics, University of Ottawa（Ottawa大学经济学系）；Department of Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicine at Tulane University（Tulane大学生物统计学与数据科学系）；Department of Statistics, Western Michigan University（西方密苏里大学统计学系）；Department of Economics, Western Michigan University（西方密苏里大学经济学系）；School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley（德克萨斯里奥格兰德谷大学数学与统计学系）；Robinson College of Business, Georgia State University（佐治亚州立大学罗宾逊商学院）；Department of Mathematics & Statistics, University of North Florida（北佛罗里达大学数学与统计学系）；Department of Epidemiology and Biostatistics, University of Arizona（亚利桑那大学流行病学与生物统计学系）；Department of Statistics and Actuarial Science, University of Ghana（加纳大学统计学与精算科学系）
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data
Universität Heidelberg（海德堡大学）
心电图（ECG）信号的分析可能是耗时的，因为它是由心脏病专家手动执行的。因此，越来越多地提出通过机器学习（ML）分类的自动化，这将允许ML模型学习心跳的特征并检测异常。缺乏可解释性阻碍了深度学习在医疗保健中的应用。通过这些模型的可解释性，我们将了解机器学习算法如何做出决策，以及分类遵循什么模式。本文基于最先进的模型构建了卷积神经网络（CNN）和长短期记忆（LSTM）分类器，并将它们的性能和可解释性与浅层分类器进行了比较。在这里，利用全局和局部可解释性方法来理解整个数据集上因变量和自变量之间的相互作用，并分别检查每个样本中的模型决策。部分依赖图、Shapley加法分解、置换特征重要性和梯度加权类激活图（Grad-Cam）是在分类ECG节律的时间序列ML模型上实现的四种可解释性技术。特别是，我们利用梯度凸轮，这是一个本地的可解释性技术，并检查其可解释性是否在每个类内正确和错误分类的ECG搏动之间的变化。此外，使用K折交叉验证和Leave Groups Out技术对分类器进行评估，并且我们使用非参数统计检验来检查差异是否显著。研究发现，Grad-CAM是解释所提出的CNN和LSTM模型预测的最有效的可解释性技术。我们的结论是，所有高性能分类器在进行预测时都会考虑ECG节律的QRS波群。
GRASP: group-Shapley feature selection for patients
1 Chinese Academy of Medical Sciences \& Peking Union Medical College, Beijing, China 2 Queen's University, Belfast, United Kingdom. 3 Heidelberg University, Heidelberg, Germany. Email
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
Computer Science Department, College of Computers and Information Technology, Taif University（计算机科学系，计算机与信息科技学院，泰夫大学）；Department of Computer Science, Taif University（计算机科学系，泰夫大学）
Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data
Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany（慕尼黑路德维希-马克西米利安大学儿童与青少年精神病学系医学成像人工智能（AI-Med））
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing
Southwest Petroleum University（西南石油大学）；Institute for Artificial Intelligence, Southwest Petroleum University（西南石油大学人工智能研究院）；State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation（油气藏地质及开发工程国家重点实验室）
