Papers Interpretable Machine Learning
“Interpretable Machine Learning” 태그가 달린 논문 632편 · 필터 해제
Improving Asset Allocation in a Fast Moving Consumer Goods B2B Company: An Interpretable Machine Learning Framework for Commercial Cooler Assignment Based on Multi-Tier Growth Targets
In the fast-moving consumer goods (FMCG) industry, deciding where to place physical assets, such as commercial beverage coolers, can directly impact revenue growth and execution efficiency. Although churn prediction and …
Interpretable Machine LearningPattern Recognition of Scrap Plastic Misclassification in Global Trade Data
We propose an interpretable machine learning framework to help identify trade data discrepancies that are challenging to detect with traditional methods. Our system analyzes trade data to find a novel inverse price-volum…
Interpretable Machine LearningCombining digital data streams and epidemic networks for real time outbreak detection
Responding to disease outbreaks requires close surveillance of their trajectories, but outbreak detection is hindered by the high noise in epidemic time series. Aggregating information across data sources has shown great…
Interpretable Machine LearningKolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws
Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics directly from data, while strictly adhering to the Arrhenius and mass action laws. H…
Interpretable Machine LearningAn Explainable and Fair AI Tool for PCOS Risk Assessment: Calibration, Subgroup Equity, and Interactive Clinical Deployment
This paper presents a fairness-audited and interpretable machine learning framework for predicting polycystic ovary syndrome (PCOS), designed to evaluate model performance and identify diagnostic disparities across patie…
Interpretable Machine LearningInterpretable Machine Learning for Reservoir Water Temperatures in the U.S. Red River Basin of the South
Accurate prediction of Reservoir Water Temperature (RWT) is vital for sustainable water management, ecosystem health, and climate resilience. Yet, prediction alone offers limited insight into the governing physical proce…
Interpretable Machine LearningWildfireGenome: Interpretable Machine Learning Reveals Local Drivers of Wildfire Risk and Their Cross-County Variation
Current wildfire risk assessments rely on coarse hazard maps and opaque machine learning models that optimize regional accuracy while sacrificing interpretability at the decision scale. WildfireGenome addresses these gap…
Interpretable Machine LearningInterpretable Machine Learning for Cognitive Aging: Handling Missing Data and Uncovering Social Determinant
Early detection of Alzheimer's disease (AD) is crucial because its neurodegenerative effects are irreversible, and neuropathologic and social-behavioral risk factors accumulate years before diagnosis. Identifying higher-…
Interpretable Machine LearningAutomatic Piecewise Linear Regression for Predicting Student Learning Satisfaction
Although student learning satisfaction has been widely studied, modern techniques such as interpretable machine learning and neural networks have not been sufficiently explored. This study demonstrates that a recent mode…
Interpretable Machine LearningInterpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter panel for 2010-2023 is constructed from Crunchbase and matched to U.S. …
Interpretable Machine LearningStructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R
We present StructuralDecompose, an R package for modular and interpretable time series decomposition. Unlike existing approaches that treat decomposition as a monolithic process, StructuralDecompose separates the analysi…
Interpretable Machine LearningAnomaly DetectionPilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
With the rapid growth of the aviation industry, there is a need for a large number of flight crew. How to select the right pilots in a cost-efficient manner has become an important research question. In the current study…
Interpretable Machine LearningInterpretable Machine Learning for Life Expectancy Prediction: A Comparative Study of Linear Regression, Decision Tree, and Random Forest
Life expectancy is a fundamental indicator of population health and socio-economic well-being, yet accurately forecasting it remains challenging due to the interplay of demographic, environmental, and healthcare factors.…
Interpretable Machine LearningFeature ImportanceDownscaling human mobility data based on demographic socioeconomic and commuting characteristics using interpretable machine learning methods
Understanding urban human mobility patterns at various spatial levels is essential for social science. This study presents a machine learning framework to downscale origin-destination (OD) taxi trips flows in New York Ci…
Interpretable Machine LearningProjective Kolmogorov Arnold Neural Networks (P-KANs): Entropy-Driven Functional Space Discovery for Interpretable Machine Learning
Kolmogorov-Arnold Networks (KANs) relocate learnable nonlinearities from nodes to edges, demonstrating remarkable capabilities in scientific machine learning and interpretable modeling. However, current KAN implementatio…
Interpretable Machine LearningDiscovering Association Rules in High-Dimensional Small Tabular Data
Association Rule Mining (ARM) aims to discover patterns between features in datasets in the form of propositional rules, supporting both knowledge discovery and interpretable machine learning in high-stakes decision-maki…
Interpretable Machine LearningTSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data
Quality of Experience (QoE) modeling is crucial for optimizing video streaming services to capture the complex relationships between different features and user experience. We propose a novel approach to QoE modeling in …
Interpretable Machine LearningPredicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
Purpose: Chest X-rays are essential for diagnosing pulmonary conditions, but limited access in resource-constrained settings can delay timely diagnosis. Electrocardiograms (ECGs), in contrast, are widely available, non-i…
Interpretable Machine LearningTop-$k$ Feature Importance Ranking
Accurate ranking of important features is a fundamental challenge in interpretable machine learning with critical applications in scientific discovery and decision-making. Unlike feature selection and feature importance,…
Interpretable Machine LearningFeature ImportanceFast and Interpretable Machine Learning Modelling of Atmospheric Molecular Clusters
Understanding how atmospheric molecular clusters form and grow is key to resolving one of the biggest uncertainties in climate modelling: the formation of new aerosol particles. While quantum chemistry offers accurate in…
Interpretable Machine LearningMetric Learning