Papers Interpretable Machine Learning
“Interpretable Machine Learning” 태그가 달린 논문 632편 · 필터 해제
Causal rule ensemble approach for multi-arm data
Heterogeneous treatment effect (HTE) estimation is critical in medical research. It provides insights into how treatment effects vary among individuals, which can provide statistical evidence for precision medicine. Whil…
Interpretable Machine LearningA Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis
Modeling path loss in indoor LoRaWAN technology deployments is inherently challenging due to structural obstructions, occupant density and activities, and fluctuating environmental conditions. This study proposes a two-s…
Hybrid Machine LearningIndoor Localizationinput filteringInterpretable Machine Learning+1Towards Simple Machine Learning Baselines for GNSS RFI Detection
Machine learning research in GNSS radio frequency interference (RFI) detection often lacks a clear empirical justification for the choice of deep learning architectures over simpler machine learning approaches. In this w…
Deep LearningInterpretable Machine LearningInterpretable Machine Learning in Physics: A Review
Machine learning is increasingly transforming various scientific fields, enabled by advancements in computational power and access to large data sets from experiments and simulations. As artificial intelligence (AI) cont…
Interpretable Machine Learningscientific discoveryKernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
Machine learning and high-throughput experimentation have greatly accelerated the discovery of mixed metal oxide catalysts by leveraging their compositional flexibility. However, the lack of established synthesis routes …
Experimental DesignInterpretable Machine LearningPredicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning
Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently underdiagnosed condition characterized by persistent, intrusive preoccupations with perceived defects in physical appearance. In this extended analysis,…
Interpretable Machine LearningXAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction skills are increasing with advances in nume…
Interpretable Machine LearningPredicting and Understanding College Student Mental Health with Interpretable Machine Learning
Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is ch…
Interpretable Machine LearningDiagnostic-free onboard battery health assessment
Diverse usage patterns induce complex and variable aging behaviors in lithium-ion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery's curre…
Battery diagnosisDecoderDiagnosticInterpretable Machine Learning+1A Frank System for Co-Evolutionary Hybrid Decision-Making
We introduce Frank, a human-in-the-loop system for co-evolutionary hybrid decision-making aiding the user to label records from an un-labeled dataset. Frank employs incremental learning to ``evolve'' in parallel with the…
Decision MakingFairnessIncremental LearningInterpretable Machine LearningNear Optimal Decision Trees in a SPLIT Second
Decision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is fast but provably suboptimal. Recent app…
Interpretable Machine LearningAn Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions
Solar flares are defined as outbursts on the surface of the Sun. They occur when energy accumulated in magnetic fields enclosing solar active regions (ARs) is abruptly expelled. Solar flares and associated coronal mass e…
Binary ClassificationDecision MakingInterpretable Machine LearningInterpretable Machine Learning for Kronecker Coefficients
We analyze the saliency of neural networks and employ interpretable machine learning models to predict whether the Kronecker coefficients of the symmetric group are zero or not. Our models use triples of partitions as in…
Interpretable Machine LearningClassifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
Virus-like particles (VLPs) are valuable for vaccine development due to their immune-triggering properties. Understanding their stoichiometry, the number of protein subunits to form a VLP, is critical for vaccine optimis…
Interpretable Machine LearningInvestigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning
This study bridges the knowledge gap on how personal factors affect building occupants' responses in active shooter situations by applying interpretable machine learning methods to data from 107 participants. The persona…
Interpretable Machine LearningHigh-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture
The removal of leaked radioactive iodine isotopes in humid environments holds significant importance in nuclear waste management and nuclear accident mitigation. In this study, high-throughput computational screening and…
Feature ImportanceInterpretable Machine LearningUnfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models
We use a tensor unfolding technique to prove a new identifiability result for discrete bipartite graphical models, which have a bipartite graph between an observed and a latent layer. This model family includes popular m…
Interpretable Machine LearningMedical DiagnosisPrompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
We present a simple usage of pre-trained Vision Transformers (ViTs) for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as different bird species or do…
Explainable Artificial Intelligence (XAI)Explainable ModelsFine-Grained Image ClassificationInterpretable Machine Learning+1Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system
This article discusses the integration of the Artificial Bee Colony (ABC) algorithm with two supervised learning methods, namely Artificial Neural Networks (ANNs) and Adaptive Network-based Fuzzy Inference System (ANFIS)…
feature selectionInterpretable Machine LearningThe explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR
Explainable AI (XAI) provides methods to understand non-interpretable machine learning models. However, we have little knowledge about what legal experts expect from these explanations, including their legal compliance w…
Interpretable Machine Learning