Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable for domain experts. Spectroscopy data, which records the interaction between matter and electromagnetic radiation, particularly holds a lot of information in a single sample. Since acquiring such high-dimensional data is a complex task, it is crucial to exploit the best analytical tools to extract necessary information. In this paper, we investigate the most commonly used feature selection techniques and introduce applying recent explainable AI techniques to interpret the prediction outcomes of high-dimensional and limited spectral data. Interpretation of the prediction outcome is beneficial for the domain experts as it ensures the transparency and faithfulness of the ML models to the domain knowledge. Due to the instrument resolution limitations, pinpointing important regions of the spectroscopy data creates a pathway to optimize the data collection process through the miniaturization of the spectrometer device. Reducing the device size and power and therefore cost is a requirement for the real-world deployment of such a sensor-to-prediction system as a whole. We specifically design three different scenarios to ensure that the evaluation of ML models is robust for the real-time practice of the developed methodologies and to uncover the hidden effect of noise sources on the final outcome.
Enhancing Diagnostic Accuracy for Urinary Tract Disease through Explainable SHAP-Guided Feature Selection and Classification
Labcin - Nature Inspired Computing Laboratory（Labcin - 自然启发计算实验室）；Federal University of Espírito Santo（巴西圣埃斯皮里图联邦大学）；Graduate Program in Computer Science（计算机科学研究生项目）
Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries
Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia (UdeA)（热带疾病生物物理学马克斯·普朗克双人组，安蒂奥基亚大学）；Grupo de Física Atómica y Molecular, Instituto de Física, University of Antioquia (UdeA)（原子与分子物理组，物理研究所，安蒂奥基亚大学）；Graduate School and Research, Arcada University of Applied Sciences（应用科学大学研究生院）；VTT Technical Research Centre of Finland Ltd（芬兰技术研究中心）；Department of Internal Medicine, School of Medicine, University of Antioquia（内科学系，医学院，安蒂奥基亚大学）；Hospital Alma Mater de Antioquia, University of Antioquia（安蒂奥基亚大学阿尔玛 Mater 医院）
Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values. The framework has been implemented in the IBM Process Mining…
Predictive process analytics focuses on predicting the future states of running instances of a business process. While advanced machine learning techniques have been used to increase accuracy of predictions, the resulting predictive models lack transparency. Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models. However, it is unclear how fit for purpose these methods are in explaining process predictive models. In this paper, we draw on evaluation measures used in the field of explainable AI and propose functionally-grounded evaluation …
Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair results, which can even lead to situations of discrimination (e.g. gender, social or racial). In order to illustrate how important is to count with a diverse training dataset to avoid bias, we manipulate a well-known facial expression recognition dataset to explore gender bias and…
UiT The Arctic University of Norway（挪威北极大学）；Technical University of Berlin（柏林工业大学）
The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the predictive performance of the model, or produce explanations that are not transparent, trustworthy or do not capture the diversity of the data. To address these shortcomings, we propose ProtoVAE, a variational autoencoder-based framework that learns class-specific prototypes in an end-to-end manner and enforces trustworthiness and diversity by regularizing the representation space and introducing an orthonormality c…
myInvenio（myInvenio 公司）；University of Padua（帕多瓦大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）
Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanatio…
In this work, a machine learning approach is developed for predicting the outcomes of football matches. The novelty of this research lies in the utilisation of the Kelly Index to first classify matches into categories where each one denotes the different levels of predictive difficulty. Classification models using a wide suite of algorithms were developed for each category of matches in order to determine the efficacy of the approach. In conjunction to this, a set of previously unexplored features were engineering including Elo-based variables. The dataset originated from the Premier League ma…
Explainable AI and Machine Learning for Exam-based Student Evaluation: Causal and Predictive Analysis of Socio-academic and Economic Factors
Institute of Information Technology Jahangirnagar University（贾汗吉尔纳加尔大学信息科技学院）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Cefalo Dhaka Bangladesh（达卡 Bangladesh 的 Cefalo）
In this paper, we describe our Knowledge Tracing model for the 2020 NeurIPS Education Challenge. We used a combination of 22 models to predict whether the students will answer a given question correctly or not. Our combination of different approaches allowed us to get an accuracy higher than any of the individual models, and the variation of our model types gave our solution better explainability, more alignment with learning science theories, and high predictive power.
Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
Several decision points exist in business processes (e.g., whether a purchase order needs a manager's approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than $500 needs a manager approval). Decision mining in process mining aims to describe/predict the routing of a process instance at a decision point of the process. By predicting the decision, one can take proactive actions to improve the process. For instance, when a bottleneck is developing in one of the possible decisions, one can predict the deci…
paluno - The Ruhr Institute for Software Technology（帕鲁诺——鲁尔软件技术研究所）
Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of interpretability, which limits their adoption in practice. We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models. Existing solutions focus on giving factual explanations.While factual explanations can be helpful, humans typically do not ask why a particular prediction was mad…
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…
Meta-learning is a field that aims at discovering how different machine learning algorithms perform on a wide range of predictive tasks. Such knowledge speeds up the hyperparameter tuning or feature engineering. With the use of surrogate models various aspects of the predictive task such as meta-features, landmarker models e.t.c. are used to predict the expected performance. State of the art approaches are focused on searching for the best meta-model but do not explain how these different aspects contribute to its performance. However, to build a new generation of meta-models we need a deeper …
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）
The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine-granular data, process mining has recently emerged as a promising research discipline. As an important branch of process mining, predictive business process management, pursues the objective to generate forward-looking, predictive insights to shape business processes. In this study, we propose a conceptual framework sought to establish and promote understanding of decision-making environment, underlying business pro…
Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
School of Engineering, University of Edinburgh（爱丁堡大学工程学院）；Causality in Healthcare AI Hub (CHAI)（医疗因果AI枢纽）；Advanced Care Research Centre, Usher School of Population Health Sciences, University of Edinburgh（先进护理研究中心，乌瑟人口健康科学学院，爱丁堡大学）；Centre for Medical Informatics, Usher School of Population Health Sciences, University of Edinburgh（医学信息学中心，乌瑟人口健康科学学院，爱丁堡大学）
Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions. We consider the problem of building explainable classifiers from multi-variate time series data. A key criterion to understand such predictive models involves elucidating and quantifying the contribution of time varying input variables to the classification. Hence, we introduce a novel, modular, convolution-based feature extraction and attention mechanism that simultaneously identifies the variabl…
Northeastern University（东北大学）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Bay Area Environmental Research Institute（湾区环境研究所）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to descr…
Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
System Dynamics, Chemnitz University of Technology, Chemnitz, Germany（化学特伦茨大学系统动力学系）；Department of Bioresources, Fraunhofer Institute for Molecular Biology and Applied Ecology（弗劳恩霍夫分子生物学与应用生态学研究所生物资源系）
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；University of South Carolina（南卡罗来纳大学）
The vast majority of research on explainability focuses on post-explainability rather than explainable modeling. Namely, an explanation model is derived to explain a complex black box model built with the sole purpose of achieving the highest performance possible. In part, this trend might be driven by the misconception that there is a trade-off between explainability and accuracy. Furthermore, the consequential work on Shapely values, grounded in game theory, has also contributed to a new wave of post-explainability research on better approximations for various machine learning models, includ…
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…
Deep visual models have widespread applications in high-stake domains. Hence, their black-box nature is currently attracting a large interest of the research community. We present the first survey in Explainable AI that focuses on the methods and metrics for interpreting deep visual models. Covering the landmark contributions along the state-of-the-art, we not only provide a taxonomic organization of the existing techniques, but also excavate a range of evaluation metrics and collate them as measures of different properties of model explanations. Along the insightful discussion on the current …
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency. Explainable AI (XAI) methods are used to improve the interpretability of these complex models, and in doing so improve transparency. However, the inherent fitness of these explainable methods can be hard to evaluate. In particular, methods to evaluate the fidelity of the explanation to the underlying black box require further development, especially for tabular data. In this paper, we (a) prop…
We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as Early Warning Scores …
The University of Hong Kong（香港大学）；Suoxinda Holdings Limited（索信达控股有限公司）
In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a user. The explanations can be either obtained by post-hoc diagnostics after fitting a relatively complex model or embedded into an intrinsically interpretable model. In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI). This model architecture is intrinsically interpretable, as it additively consists of the user and item…
This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. Our working hypothesis is that distributional sparsity, as facilitated by the Dirichlet prior, will encourage disentangled feature learning for the complex task of multi-label classification of CXR images. The DirVAE is trained using CXR images from the CheXpert database, and the predictive capacity of multi-modal latent representations learned by DirVAE models is investigated through implementation of an auxiliary multi-label classificatio…
Predictive Modeling and Explainable AI for Veterinary Safety Profiles, Residue Assessment, and Health Outcomes Using Real-World Data and Physicochemical Properties
DATA Consortium and FARAD Program, Kansas State University, Olathe, KS, USA（1DATA Consortium和FARAD计划，堪萨斯州立大学）；Department of Computer Science, Kansas State University, Manhattan, KS, USA（计算机科学系，堪萨斯州立大学）；Department of Statistics, Kansas State University, Manhattan, KS, USA（统计学系，堪萨斯州立大学）；Department of Mathematics, Kansas State University, Olathe, KS, USA（数学系，堪萨斯州立大学）
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）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）
This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and shop-floor data obtained from various enterprise information systems, a deep learning model was applied to predict the process outcomes. Since this study aims to operationalize the delivered predictive insights by embedding them into decision-making processes, it is essential to …
Remaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making
Nepal Engineering College, Pokhara University（博卡拉大学尼泊尔工程学院）；University of Delaware（特拉华大学）；Delft University of Technology（代尔夫特理工大学）；The Hague University of Applied Science（海牙应用科学大学）；Institute of Engineering, Pulchowk Campus, Tribhuvan University（特里布文大学工程学院普尔乔克校区）
