Machine learning is becoming increasingly prevalent for tackling challenges in earthquake engineering and providing fairly reliable and accurate predictions. However, it is mostly unclear how decisions are made because machine learning models are generally highly sophisticated, resulting in opaque black-box models. Machine learning models that are naturally interpretable and provide their own decision explanation, rather than using an explanatory, are more accurate in determining what the model actually computes. With this motivation, this study aims to develop a fully explainable machine learning model to predict the deformation capacity of non-ductile reinforced concrete shear walls based on experimental data collected worldwide. The proposed Explainable Boosting Machines (EBM)-based model is an interpretable, robust, naturally explainable glass-box model, yet provides high accuracy comparable to its black-box counterparts. The model enables the user to observe the relationship between the wall properties and the deformation capacity by quantifying the individual contribution of each wall property as well as the correlations among them. The mean coefficient of determination R2 and the mean ratio of predicted to actual value based on the test dataset are 0.92 and 1.05, respectively. The proposed predictive model stands out with its overall consistency with scientific knowledge, practicality, and interpretability without sacrificing high accuracy.
Statistical Inference for Explainable Boosting Machines
School of Economics, Fudan University（复旦大学经济学院）；Department of Statistics and Data Science, The Wharton School, University of Pennsylvania（宾夕法尼亚大学沃顿商学院统计与数据科学系）
Maastricht University（马斯特里赫特大学）；University of Amsterdam（阿姆斯特丹大学）
Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step further towards exploring the use of non-linear interpretable machine learning (ML) models in classification problems. ML models can enhance the ability to accurately predict the occurrence of different behaviors by recognizing complicated patterns between variables in data. To evaluate this, the performance of various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. After…
Application of Machine Learning algorithms to the medical domain is an emerging trend that helps to advance medical knowledge. At the same time, there is a significant a lack of explainable studies that promote informed, transparent, and interpretable use of Machine Learning algorithms. In this paper, we present explainable multi-class classification of the Covid-19 mental health data. In Machine Learning study, we aim to find the potential factors to influence a personal mental health during the Covid-19 pandemic. We found that Random Forest (RF) and Gradient Boosting (GB) have scored the hig…
It becomes a significant challenge to predict customer behavior and retain an existing customer with the rapid growth of digitization which opens up more opportunities for customers to choose from subscription-based products and services model. Since the cost of acquiring a new customer is five-times higher than retaining an existing customer, henceforth, there is a need to address the customer churn problem which is a major threat across the Industries. Considering direct impact on revenues, companies identify the factors that increases the customer churn rate. Here, key objective of the pape…
As more and more artificial intelligence (AI) technologies move from the laboratory to real-world applications, the open-set and robustness challenges brought by data from the real world have received increasing attention. Data augmentation is a widely used method to improve model performance, and some recent works have also confirmed its positive effect on the robustness of AI models. However, most of the existing data augmentation methods are heuristic, lacking the exploration of their internal mechanisms. We apply the explainable artificial intelligence (XAI) method, explore the internal me…
Microsoft（微软）；University of Pennsylvania（宾夕法尼亚大学）；Stanford University（斯坦福大学）
We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while protecting privacy. Our experiments on multiple classification and regression datasets show that DP-EBM models suffer surprisingly little accuracy loss even with strong differential privacy guarantees. In addition to high accuracy, two other benefits of applying DP to EBMs are: a) trained models provide exact global and local interpretability, which is often important in settings where differential privacy is needed; and …
Blue Yonder GmbH（蓝扬德有限公司）；IUBH Internationale Hochschule（IUBH国际大学）
Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction w…
An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction
Institute of Information Technology（信息技术学院）；University of Dhaka（达卡大学）；Accident Research Institute（事故研究所）；Bangladesh University of Engineering and Technology（孟加拉工程技术大学）
University of Missouri-Columbia（密苏里大学哥伦比亚分校）；Northeastern University（东北大学）
Cybersickness can be characterized by nausea, vertigo, headache, eye strain, and other discomforts when using virtual reality (VR) systems. The previously reported machine learning (ML) and deep learning (DL) algorithms for detecting (classification) and predicting (regression) VR cybersickness use black-box models; thus, they lack explainability. Moreover, VR sensors generate a massive amount of data, resulting in complex and large models. Therefore, having inherent explainability in cybersickness detection models can significantly improve the model's trustworthiness and provide insight into …
The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical contexts. However, GWR suffers from the problem that classical linear regressions, which compose the GWR model, are more prone to be underfitting, especially for significant volume and complex nonlinear data, causing inferior comparative performance. Nevertheless, some advanced models, such as the decision tree and the support vector machine, can learn features from complex data more effectively while they cannot pro…
Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study
Institute of Computer Science, Foundation for Research and Technology（希腊研究与技术基金会计算机科学研究所）；Technical University of Crete（克里特技术大学）；University of Crete（克里特大学）
Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States
University of Tennessee Graduate School（田纳西大学研究生院）
University of Texas at Dallas（德克萨斯大学达拉斯分校）；Amazon（亚马逊）；Indiana University（印第安纳大学）；Oregon State University（俄勒冈州立大学）
Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of PLMs -- interpretability. In this paper we consider the problem of compressing a large set of learned trees into a single explainable model. To this effect, we propose CoTE -- Compression of Tree Ensembles -- that produces a single small decision list as a compressed representation. CoTE first converts the trees to decision lists and then performs…
Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）；Microsoft Research（微软研究院）；Foundation for Health Care Quality（医疗质量基金会）
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through better understanding of risk factors, heightened surveillance, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. For three types of complications we identify and study the most important risk factors using Explainable Boosting Machine (EBM), a glass box model, in order to gain intelligibility: (i) S…
Knowledge-Guided Machine Learning: Illustrating the use of Explainable Boosting Machines to Identify Overshooting Tops in Satellite Imagery
The Courant Institute of Mathematical Sciences, New York University（Courant数学科学研究所，纽约大学）；Department of Statistics, Colorado State University（统计学系，科罗拉多州立大学）；Cooperative Institute for Research in the Atmosphere, Colorado State University（大气研究合作研究所，科罗拉多州立大学）；Department of Electrical and Computer Engineering, Colorado State University（电气与计算机工程系，科罗拉多州立大学）；Department of Mathematics, Colorado State University（数学系，科罗拉多州立大学）；School of Earth and Environmental Sciences, Seoul National University（地球与环境科学学院，首尔国立大学）
University of Ottawa（渥太华大学）；Dalhousie University（达尔豪斯大学）
Machine Learning applications have brought new insights into a secondary analysis of medical data. Machine Learning helps to develop new drugs, define populations susceptible to certain illnesses, identify predictors of many common diseases. At the same time, Machine Learning results depend on convolution of many factors, including feature selection, class (im)balance, algorithm preference, and performance metrics. In this paper, we present explainable multi-class classification of a large medical data set. We in details discuss knowledge-based feature engineering, data set balancing, best mod…
National Observatory of Athens（雅典国家天文台）；National Technical University of Athens（雅典国立技术大学）
Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the insects in the field is crucial for effective farm interventions. Meteo-climatic and vegetation conditions have been identified as key drivers of crop pest abundance. In this work, we applied an interpretable classifier, i.e., Explainable Boosting Machine, which uses earth observation vegetation indices, numerical weather predictions and insect trap catches to predict the onset of bollworm harmfulness in cotton fields i…
Unveiling Hidden Factors: Explainable AI for Feature Boosting in Speech Emotion Recognition
University of Québec (TÉLUQ)（魁北克大学（TÉLUQ））；Concordia University（康考迪亚大学）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程学院）；University of Québec at Montréal（蒙特利尔魁北克大学）；Centre for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices（自杀、伦理问题与临终实践研究与干预中心）
PwC Switzerland（普华永道瑞士）；University of Agder（阿格德大学）
Tsetlin Machines (TMs) capture patterns using conjunctive clauses in propositional logic, thus facilitating interpretation. However, recent TM-based approaches mainly rely on inspecting the full range of clauses individually. Such inspection does not necessarily scale to complex prediction problems that require a large number of clauses. In this paper, we propose closed-form expressions for understanding why a TM model makes a specific prediction (local interpretability). Additionally, the expressions capture the most important features of the model overall (global interpretability). We furthe…
Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting
Canberra Health Services, Canberra Hospital（堪培拉卫生服务局，堪培拉医院）；University of Technology Sydney（悉尼科技大学）；University of New South Wales（新南威尔士大学）；The Australian National University（澳大利亚国立大学）；Obuda University（欧布达大学）
Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players
Zhili College（致理书院）；Tsinghua University（清华大学）；Research Centre for Athletic Performance and Data Science & Division of Sports Science and Physical Education（运动表现与数据科学研究中心&体育科学与体育教育系）
Faculty of Economics and Business, KU Leuven（比利时鲁汶大学经济与管理学院）；LRisk, Leuven Research Center on Insurance and Financial Risk Analysis, KU Leuven（比利时鲁汶大学保险与金融风险分析研究中心（LRisk））；École d’actuariat, Université Laval（加拿大拉瓦尔大学精算学院）；Faculty of Economics and Business, University of Amsterdam（荷兰阿姆斯特丹大学经济与管理学院）；Faculty of Economics and Business, KU Leuven, Belgium（比利时鲁汶大学经济与管理学院）；LRisk, Leuven Research Center on Insurance and Financial Risk Analysis, KU Leuven, Belgium（比利时鲁汶大学保险与金融风险分析研究中心（LRisk））
Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims. We adapt the loss functions used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros and varying exposure on the frequency side combined with scarce, but potentially long-tailed data on the severity side. A key requirement is the need for t…
Department of Artificial Intelligence, University of Malta（马耳他大学人工智能系）
With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulati…
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）
Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles
The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi（泰国国王科技大学吞武里校区能源与环境联合研究生院）；AI Research Group, Department of Civil Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi（泰国国王科技大学吞武里校区工程学院土木工程系人工智能研究组）；Center of Excellence on Energy Technology and Environment (CEE), PERDO, Ministry of Higher Education, Science, Research and Innovation (MHESI)（高等教育、科学、研究与创新部PERDO能源技术与环境卓越中心）；Department of Environmental Engineering, Faculty of Engineering, Kasetsart University（农业大学工程学院环境工程系）
Exploring Machine Learning, Deep Learning, and Explainable AI Methods for Seasonal Precipitation Prediction in South America
Laboratório de Inteligência ARtificial para Aplicações AeroEspaciais e Ambientais (LIAREA)（人工智能应用航空与环境实验室）；Coordenação de Pesquisa Aplicada e Desenvolvimento Tecnológico (COPDT)（应用研究与技术开发协调处）；Programa de Pós-Graduação em Computação Aplicada (PGCAP)（应用计算硕士项目）；Instituto Nacional de Pesquisas Espaciais (INPE)（国家空间科学研究院）；Instituto de Estudos Avançados (IEAv)（高级研究学院）
