Papers Interpretable Machine Learning
“Interpretable Machine Learning” 태그가 달린 논문 632편 · 필터 해제
Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints
Financial distress prediction remains a significant challenge in enterprise risk analysis due to the highly imbalanced nature of real-world financial datasets, where bankrupt or distressed firms typically constitute only…
Interpretable Machine LearningEnsemble LearningBoolXLLM: LLM-Assisted Explainability for Boolean Models
Interpretable machine learning aims to provide transparent models whose decision-making processes can be readily understood by humans. Recent advances in rule-based approaches, such as expressive Boolean formulas (BoolXA…
Interpretable Machine LearningInterpretable Machine Learning for Football Performance Analysis: Evidence of Limited Transferability from Elite Leagues to University Competition
Machine learning has become increasingly prevalent in football performance analysis, yet most studies prioritize predictive accuracy while implicitly assuming that learned performance determinants and their interpretatio…
Interpretable Machine LearningInterpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes
Many three-dimensional spatial fields are anisotropic, with directions of rapid and slow variation that need not align with the coordinate axes. Standard Gaussian process kernels with Automatic Relevance Determination (A…
Interpretable Machine LearningGaussian ProcessesBayesian InferenceBeyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems
The current catalyst discovery and development pipeline for energy-intensive applications like methane conversion remains bottlenecked by expensive trial-and-error experimentation, irreproducible chemical intuition, and …
Interpretable Machine LearningFeature ImportanceLocating acts of mechanistic reasoning in student team conversations with mechanistic machine learning
STEM education researchers are often interested in identifying moments of students' mechanistic reasoning for deeper analysis, but have limited capacity to search through many team conversation transcripts to find segmen…
Interpretable Machine LearningDiscovering quantum phenomena with Interpretable Machine Learning
Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent advances in variational autoencoders to demonstrate that such models c…
Interpretable Machine LearningA Tsetlin Machine-driven Intrusion Detection System for Next-Generation IoMT Security
The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it also introduces serious cybersecurity and…
Interpretable Machine LearningMulti-class ClassificationBinary ClassificationIntrusion DetectionDecoding AI Authorship: Can LLMs Truly Mimic Human Style Across Literature and Politics?
Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large language models (LLMs), including GPT-4o, Gemini 1.5 Pro, and Claude Sonnet 3.…
Interpretable Machine LearningFeature ImportanceEnhancing LIME using Neural Decision Trees
Interpreting complex machine learning models is a critical challenge, especially for tabular data where model transparency is paramount. Local Interpretable Model-Agnostic Explanations (LIME) has been a very popular fram…
Interpretable Machine LearningAn Interpretable Machine Learning Framework for Non-Small Cell Lung Cancer Drug Response Analysis
Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatment strategies are surgery, chemotherapy, and radiation which aren't t…
Interpretable Machine LearningIntegrating Explainable Machine Learning and Mixed-Integer Optimization for Personalized Sleep Quality Intervention
Sleep quality is influenced by a complex interplay of behavioral, environmental, and psychosocial factors, yet most computational studies focus mainly on predictive risk identification rather than actionable intervention…
Interpretable Machine LearningSleep QualityTT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
Interpretable machine learning is essential in high-stakes domains where decision-making requires accountability, transparency, and trust. While rule-based models offer global and exact interpretability, learning rule se…
Interpretable Machine LearningA Data-Driven Approach to Support Clinical Renal Replacement Therapy
This study investigates a data-driven machine learning approach to predict membrane fouling in critically ill patients undergoing Continuous Renal Replacement Therapy (CRRT). Using time-series data from an ICU, 16 clinic…
Interpretable Machine LearningOn the Limits of Interpretable Machine Learning in Quintic Root Classification
Can Machine Learning (ML) autonomously recover interpretable mathematical structure from raw numerical data? We aim to answer this question using the classification of real-root configurations of polynomials up to degree…
Interpretable Machine LearningKnowledge DistillationActive Value Querying to Minimize Additive Error in Subadditive Set Function Learning
Subadditive set functions play a pivotal role in computational economics (especially in combinatorial auctions), combinatorial optimization or artificial intelligence applications such as interpretable machine learning. …
Interpretable Machine LearningPredicting Multi-Drug Resistance in Bacterial Isolates Through Performance Comparison and LIME-based Interpretation of Classification Models
The rise of Antimicrobial Resistance, particularly Multi-Drug Resistance (MDR), presents a critical challenge for clinical decision-making due to limited treatment options and delays in conventional susceptibility testin…
Interpretable Machine LearningMachine Learning in Epidemiology
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous a…
Interpretable Machine LearningHyperparameter OptimizationLinguistic Indicators of Early Cognitive Decline in the DementiaBank Pitt Corpus: A Statistical and Machine Learning Study
Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically interpretable markers of dementia can support transparent and clinically gr…
Interpretable Machine LearningFeature ImportanceCounterfactual Maps: What They Are and How to Find Them
Counterfactual explanations are a central tool in interpretable machine learning, yet computing them exactly for complex models remains challenging. For tree ensembles, predictions are piecewise constant over a large col…
Interpretable Machine Learning