Papers Interpretable Machine Learning
“Interpretable Machine Learning” 태그가 달린 논문 632편 · 필터 해제
Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was establish…
Interpretable Machine LearningHyperparameter OptimizationSearch Strategies for Optimal Classification and Regression Trees
Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging. While recent work has proposed a variety of search str…
Interpretable Machine LearningNMINE: Normalized Mutual Information Neural Estimation
Mutual information is a general measure of statistical dependence that captures both linear and nonlinear relationships between random variables. For continuous and multidimensional variables For continuous multidimensio…
Interpretable Machine LearningComplexity-Budgeted, Interaction-Aware Interpretable Model for Tabular Data
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variabl…
Interpretable Machine LearningA Physics-Regulated Neural Framework for Learning 3D Grain Growth Dynamics
Grain growth is governed by the reduction in grain boundary energy and exhibits well-established statistical scaling laws. Developing data-driven surrogates that preserve these physical invariants while remaining computa…
Interpretable Machine LearningInterpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Introduction: Objective neuroimaging biomarkers may improve Parkinson's disease motor assessment by capturing brain variation not directly observable from clinical examination. We used interpretable machine learning to p…
Interpretable Machine LearningPredicting Poets' Origins from Verse: A Computational Analysis of Regional Linguistic Fingerprints in the Complete Tang Poems
We ask whether the geographic origin of Tang-dynasty poets leaves a detectable linguistic trace in their work. Aggregating every poem attributed to each author in the Complete Tang Poems (Quan Tang Shi) and linking poets…
Interpretable Machine LearningMulti-class ClassificationIt's Much Easier for Neural Networks to learn Game of Life Dynamics with the Right Activation Function: Polynomial Kolmogorov-Arnold Networks
Previous work has found a gap between the scale of neural networks that reliably learn Conway's Game of Life, and minimal networks capable of representing the classic cellular automaton with hard-coded parameter values. …
Interpretable Machine LearningInterpretable Factor Decomposition for Decision Intelligence in Large-Scale Financial Markets: Evidence from China's A-Share Market
We present an interpretable machine learning pipeline to decompose Cross-Sectional Equity Return Predictability into auditable factor contribution. We apply an XGBoost model with TreeSHAP attribution and conduct stress t…
Interpretable Machine LearningThe Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics
As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and controlling their computations. However, interpretability lacks general theories t…
Interpretable Machine LearningLearning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia
Childhood asthma is a common illness exacerbated by air pollution as well as meteorological and neighborhood-level socioeconomic factors. Modeling asthma exacerbation (AE) in large spatiotemporal datasets requires disent…
Interpretable Machine LearningShaplEIG: Bayesian Experimental Design for Shapley Value Estimation
Shapley values are a principled attribution measure widely used in interpretable machine learning, but their exact computation scales exponentially with the number of players, motivating a wide range of approximation met…
Interpretable Machine LearningFeature ImportanceBeyond Additive Decompositions: Interpretability Through Separability
Interpretable machine learning requires models that are accurate and structurally faithful to the data. Existing explainability methods rely heavily on additive representations (e.g., Generalized Additive Models (GAMs), …
Interpretable Machine LearningPosition: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods
Advances in machine learning and computational power have unlocked the predictive potential of the human genome, yet biologists now demand that these models also elucidate the underlying biological mechanisms. While inte…
Interpretable Machine LearningWhen Interpretability Becomes a Liability: Adversarial Attacks on CBM Concept Layers
Concept Bottleneck Models (CBMs) have emerged as a cornerstone approach for interpretable machine learning, providing human-understandable intermediate representations through explicit concept activations. However, this …
Interpretable Machine LearningAdversarial RobustnessClassification and detection of multiple UAVs using rational Gaussian wavelet neural networks
The detection of unmanned aerial vehicles (UAVs) is important for the protection of civilian and military infrastructure. In this paper we propose a cost effective UAV detection system using sound signals obtained from m…
Interpretable Machine LearningExploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We present a systematic feature-based analysis framework leveraging perceptu…
Interpretable Machine LearningCross-Paradigm Knowledge Distillation: A Comprehensive Study of Bidirectional Transfer Between Random Forests and Deep Neural Networks for Big Data Applications
The exponential growth of big data has intensified the need for efficient and interpretable machine learning models that can handle diverse data characteristics while maintaining computational efficiency. Knowledge disti…
Interpretable Machine LearningComputational EfficiencyKnowledge DistillationEnsemble LearningSubject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers. While previous studies have examined their neural correlates, it rema…
Interpretable Machine LearningInterpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Data
Background: Pregnancy-associated thrombotic microangiopathy (P-TMA) is rare but life-threatening. Early risk prediction before overt clinical presentation remains challenging, as the associated laboratory abnormalities a…
Interpretable Machine LearningFeature Importance