Papers Interpretable Machine Learning
“Interpretable Machine Learning” 태그가 달린 논문 632편 · 필터 해제
Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine
Addressing class imbalance is a central challenge in credit card fraud detection, as it directly impacts predictive reliability in real-world financial systems. To overcome this, the study proposes an enhanced workflow b…
Interpretable Machine LearningFeature ImportanceFraud DetectionGeneralized Optimal Classification Trees: A Mixed-Integer Programming Approach
Global optimization of decision trees is a long-standing challenge in combinatorial optimization, yet such models play an important role in interpretable machine learning. Although the problem has been investigated for s…
Interpretable Machine LearningAn efficient, accurate, and interpretable machine learning method for computing probability of failure
We introduce a novel machine learning method called the Penalized Profile Support Vector Machine based on the Gabriel edited set for the computation of the probability of failure for a complex system as determined by a t…
Interpretable Machine LearningMetabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the urgent need for objective, biology-based diagnostic frameworks in preci…
Interpretable Machine LearningMeasurement for Opaque Systems: Multi-source Triangulation with Interpretable Machine Learning
We propose a measurement framework for difficult-to-access contexts that uses indirect data traces, interpretable machine-learning models, and theory-guided triangulation to fill inaccessible measurement spaces. Many hig…
Interpretable Machine LearningCausal InferenceCross-Modal Computational Model of Brain-Heart Interactions via HRV and EEG Feature
The electroencephalogram (EEG) has been the gold standard for quantifying mental workload; however, due to its complexity and non-portability, it can be constraining. ECG signals, which are feasible on wearable equipment…
Interpretable Machine LearningSynthetic Data GenerationText Detoxification in isiXhosa and Yorùbá: A Cross-Lingual Machine Learning Approach for Low-Resource African Languages
Toxic language is one of the major barrier to safe online participation, yet robust mitigation tools are scarce for African languages. This study addresses this critical gap by investigating automatic text detoxification…
Interpretable Machine LearningText Style TransferTowards Public Administration Research Based on Interpretable Machine Learning
Causal relationships play a pivotal role in research within the field of public administration. Ensuring reliable causal inference requires validating the predictability of these relationships, which is a crucial precond…
Interpretable Machine LearningCausal InferenceThe Dependency Divide: An Interpretable Machine Learning Framework for Profiling Student Digital Satisfaction in the Bangladesh Context
Background: While digital access has expanded rapidly in resource-constrained contexts, satisfaction with digital learning platforms varies significantly among students with seemingly equal connectivity. Traditional digi…
Interpretable Machine LearningInterpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials
Digital sensing faces challenges in developing sustainable methods to extend the applicability of customized e-noses to complex body odor volatilome (BOV). To address this challenge, we developed MORE-ML, a computational…
Interpretable Machine LearningRevealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning
Given the central role of green e-molecule imports in the European energy transition, many studies optimize import pathways and identify a single cost-optimal solution. However, cost optimality is fragile, as real-world …
Interpretable Machine LearningInterpretable Machine Learning-Derived Spectral Indices for Vegetation Monitoring
Spectral indices such as NDVI have driven vegetation monitoring for decades, yet their design remains largely manual and ad hoc. Their usefulness stems not only from their empirical performance, but also from algebraic f…
Interpretable Machine LearningImputation Uncertainty in Interpretable Machine Learning Methods
In real data, missing values occur frequently, which affects the interpretation with interpretable machine learning (IML) methods. Recent work considers bias and shows that model explanations may differ between imputatio…
Interpretable Machine LearningFeature ImportanceMachine Learning Framework for Thrombosis Risk Prediction in Rotary Blood Pumps
Thrombosis in rotary blood pumps arises from complex flow conditions that remain difficult to translate into reliable and interpretable risk predictions using existing computational models. This limitation reflects an in…
Interpretable Machine LearningInterpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations
Stellar and AGN-driven feedback processes affect the distribution of gas on a wide range of scales, from within galaxies well into the intergalactic medium. Yet, it remains unclear how feedback, through its connection to…
Interpretable Machine LearningComputational Design of Low-Volatility Lubricants for Space Using Interpretable Machine Learning
The function and lifetime of moving mechanical assemblies (MMAs) in space depend on the properties of lubricants. MMAs that experience high speeds or high cycles require liquid based lubricants due to their ability to re…
Interpretable Machine LearningOptimizing Stroke Risk Prediction: A Machine Learning Pipeline Combining ROS-Balanced Ensembles and XAI
Stroke is a major cause of death and permanent impairment, making it a major worldwide health concern. For prompt intervention and successful preventative tactics, early risk assessment is essential. To address this chal…
Interpretable Machine LearningFeature EngineeringEnsemble LearningSoftly Symbolifying Kolmogorov-Arnold Networks
Kolmogorov-Arnold Networks (KANs) offer a promising path toward interpretable machine learning: their learnable activations can be studied individually, while collectively fitting complex data accurately. In practice, ho…
Interpretable Machine LearningPrivacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization
The widespread integration of Artificial Intelligence of Things (AIoT) in smart home environments has amplified the demand for transparent and interpretable machine learning models. To foster user trust and comply with e…
Interpretable Machine LearningImproving Industrial Injection Molding Processes with Explainable AI for Quality Classification
Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. …
Interpretable Machine LearningFeature Importance