Analyzing spatially varying effects is pivotal in geographic analysis. However, accurately capturing and interpreting this variability is challenging due to the increasing complexity and non-linearity of geospatial data. Recent advancements in integrating Geographically Weighted (GW) models with artificial intelligence (AI) methodologies offer novel approaches. However, these methods often focus on single algorithms and emphasize prediction over interpretability. The recent GeoShapley method integrates machine learning (ML) with Shapley values to explain the contribution of geographical features, advancing the combination of geospatial ML and explainable AI (XAI). Yet, it lacks exploration of the nonlinear interactions between geographical features and explanatory variables. Herein, an ensemble framework is proposed to merge local spatial weighting scheme with XAI and ML technologies to bridge this gap. Through tests on synthetic datasets and comparisons with GWR, MGWR, and GeoShapley, this framework is verified to enhance interpretability and predictive accuracy by elucidating spatial variability. Reproducibility is explored through the comparison of spatial weighting schemes and various ML models, emphasizing the necessity of model reproducibility to address model and parameter uncertainty. This framework works in both geographic regression and classification, offering a novel approach to understanding complex spatial phenomena.
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…
The Alan Turing Institute（艾伦·图灵研究所）；University of Warwick（华威大学）；University College London（伦敦大学学院）；University of Manchester（曼彻斯特大学）
State-of-the-art deep learning methods have shown a remarkable capacity to model complex data domains, but struggle with geospatial data. In this paper, we introduce SpaceGAN, a novel generative model for geospatial domains that learns neighbourhood structures through spatial conditioning. We propose to enhance spatial representation beyond mere spatial coordinates, by conditioning each data point on feature vectors of its spatial neighbours, thus allowing for a more flexible representation of the spatial structure. To overcome issues of training convergence, we employ a metric capturing the l…
We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets. Furthermore, XEM provides faithful explainability-by-design and manifests robust performance when faced w…
Predicting Male Domestic Violence Using Explainable Ensemble Learning and Exploratory Data Analysis
Khulna University of Engineering & Technology（库尔纳工程技术大学）；Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学）；Shahjalal University of Science and Technology（沙阿贾拉勒科学技术大学）；American International University-Bangladesh（孟加拉国美国国际大学）；The Multimedia University（多媒体大学）
Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
University of Minnesota（明尼苏达大学）；University of Cincinnati（辛辛那提大学）
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis base…
The Hong Kong University of Science and Technology（香港科技大学）
We enhance the accuracy and generalization of univariate time series point prediction by an explainable ensemble on the fly. We propose an Interpretable Dynamic Ensemble Architecture (IDEA), in which interpretable base learners give predictions independently with sparse communication as a group. The model is composed of several sequentially stacked groups connected by group backcast residuals and recurrent input competition. Ensemble driven by end-to-end training both horizontally and vertically brings state-of-the-art (SOTA) performances. Forecast accuracy improves by 2.6% over the best stati…
UiT the Arctic University of Norway（挪威北极大学（特罗姆瑟大学））；University Hospital of North Norway (UNN)（北挪威大学医院（UNN））
Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what influenced the predictions, which is critical for clinical tasks. However, existing explainability techniques lack an important component for trustworthy and reliable decision support, namely a notion of uncertainty. In this paper, we address this lack of uncertainty by proposing a deep ensemble approach where a collection of DNNs are trained indep…
Clemson University（克莱姆森大学）；AI Research Institute for Science and Engineering (AIRISE), Clemson University（克莱姆森大学人工智能科学与工程研究院）；Michigan State University（密歇根州立大学）
This paper presents the development of systematic machine learning (ML) approach to enable explainable and rapid assessment of fire resistance and fire-induced spalling of reinforced concrete (RC) columns. The developed approach comprises of an ensemble of three novel ML algorithms namely; random forest (RF), extreme gradient boosted trees (ExGBT), and deep learning (DL). These algorithms are trained to account for a wide collection of geometric characteristics and material properties, as well as loading conditions to examine fire performance of normal and high strength RC columns by analyzing…
Institute of Financial Service Analytics（金融服务分析研究所）；Alfred Lerner College of Business and Economics（阿尔弗雷德·勒纳商业与经济学院）；University Of Delaware（特拉华大学）
This paper examines two different yet related questions related to explainable AI (XAI) practices. Machine learning (ML) is increasingly important in financial services, such as pre-approval, credit underwriting, investments, and various front-end and back-end activities. Machine Learning can automatically detect non-linearities and interactions in training data, facilitating faster and more accurate credit decisions. However, machine learning models are opaque and hard to explain, which are critical elements needed for establishing a reliable technology. The study compares various machine lea…
Federal Institute of São Paulo (IFSP)（圣保罗联邦学院（IFSP））；University of São Paulo (USP)（圣保罗大学（USP））；Dalhousie University（达尔豪斯大学）
Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisions such metrics convey. This paradigm has recently shifted, and strategies beyond tables and numbers to assist in interpreting models' decisions are increasing in importance. Part of this trend, visualization techniques have been extensively used to support classification models'…
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results. We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpretability. For predictive performance, we build a structured neural network made up of ensembles of generalized additive model networks and additive index models (through explainable neural networks) using a two-stage process. This can be done using either a bo…
Thermodynamic-Inspired Explainable GeoAI: Uncovering Regime-Dependent Mechanisms in Heterogeneous Spatial Systems
Geoinformation and Big Data Research Lab, Department of Geography, Pennsylvania State University（宾夕法尼亚州立大学地理系地理信息与大数据研究实验室）；Department of Materials Science and Engineering, Pennsylvania State University（宾夕法尼亚州立大学材料科学与工程系）
Brunel University London（伦敦布鲁内尔大学）；Technical University of Darmstadt（达姆施塔特工业大学）；American International University-Bangladesh（孟加拉国国际大学）；Dhaka University of Engineering and Technology（达卡工程技术大学）；George Mason University（乔治梅森大学）
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）
University of Southern California（南加州大学）；Columbia University（哥伦比亚大学）；Tencent America（腾讯美国）
An explainable machine learning method for point cloud classification, called the PointHop method, is proposed in this work. The PointHop method consists of two stages: 1) local-to-global attribute building through iterative one-hop information exchange, and 2) classification and ensembles. In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit. When we put multiple PointHop units in cascade, the a…
Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
Department of Information Sciences and Technology（信息科学与技术系）；George Mason University（乔治·马歇尔大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Jahangirnagar University（贾汗吉尔纳加尔大学）；AIUB；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Dhaka University of Engineering and Technology（达卡工程与技术大学）
CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECG
School of Artificial Intelligence and Computer Science（人工智能与计算机科学学院）；Nantong University（南通大学）；College of Information Science and Engineering（信息科学与工程学院）；Henan University of Technology（河南理工大学）；Affiliated Hospital of Nantong University（南通大学附属医院）
Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns. This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level …
F5C-finder: An Explainable and Ensemble Biological Language Model for Predicting 5-Formylcytidine Modifications on mRNA
College of Water Resources and Architectural Engineering, Northwest A&F University（西北农林科技大学水利与建筑工程学院）；Department of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong（香港大学工程学院机械工程系）；School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University（西安交通大学电子与信息工程学院自动化科学与工程学院）
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…
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）
Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cognitive Impairment
School of Mathematical and Natural Sciences（数学与自然科学学院）；Arizona State University（亚利桑那州立大学）；Department of Mathematics（数学系）；Florida Gulf Coast University（佛罗里达海岸大学）；Department of Public Health（公共卫生系）；Julia Jones Matthews School of Population and Public Health（朱莉亚·琼斯·马修斯人口与公共卫生学院）；Texas Tech University Health Sciences Center（德克萨斯农工大学健康科学中心）
Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning
Shreya Department of Computer Science and Engineering Specialisation in Data Science and Artificial Intelligence（Shreya 计算机科学与工程系 数据科学与人工智能专攻）；Mr. Harsh Pathak Assistant Professor Department of Computer Science and Engineering（Harsh Pathak 助理教授 计算机科学与工程系）；SRM University, Sonepat, Haryana, India（SRM 大学, Sonepat, Haryana, India）
