Papers Interpretable Machine Learning
“Interpretable Machine Learning” 태그가 달린 논문 632편 · 필터 해제
An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
This paper evaluates the performance of prominent machine learning (ML) algorithms in predicting Indonesia's inflation using the payment system, capital market, and macroeconomic data. We compare the forecasting performa…
Ensemble LearningInterpretable Machine LearningregressionAn Attention-based Spatio-Temporal Neural Operator for Evolving Physics
In scientific machine learning (SciML), a key challenge is learning unknown, evolving physical processes and making predictions across spatio-temporal scales. For example, in real-world manufacturing problems like additi…
Interpretable Machine Learningmidr: Learning from Black-Box Models by Maximum Interpretation Decomposition
The use of appropriate methods of Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI) is essential for adopting black-box predictive models in fields where model and prediction explainabili…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Interpretable Machine LearningPredicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early …
feature selectionImputationInterpretable Machine LearningExplainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-base…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Interpretable Machine LearningStock Price Prediction+1Data Model Design for Explainable Machine Learning-based Electricity Applications
The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has triggered a digital transformation of the energy infrastructure …
Feature ImportanceInterpretable Machine LearningInterpretable Machine Learning for Macro Alpha: A News Sentiment Case Study
This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project's worldwide n…
Interpretable Machine LearningAre machine learning interpretations reliable? A stability study on global interpretations
As machine learning systems are increasingly used in high-stakes domains, there is a growing emphasis placed on making them interpretable to improve trust in these systems. In response, a range of interpretable machine l…
Interpretable Machine LearningMachine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients
Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vulnerability and comorbidities. Early and…
Decision MakingFeature EngineeringImputationInterpretable Machine Learning+1Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy
Brain stroke remains one of the principal causes of death and disability worldwide, yet most tabular-data prediction models still hover below the 95% accuracy threshold, limiting real-world utility. Addressing this gap, …
feature selectionInterpretable Machine LearningMissing ValuesOn the definition and importance of interpretability in scientific machine learning
Though neural networks trained on large datasets have been successfully used to describe and predict many physical phenomena, there is a sense among scientists that, unlike traditional scientific models comprising simple…
Equation DiscoveryInterpretable Machine Learningscientific discoverySymbolic RegressionEnhanced Photonic Chip Design via Interpretable Machine Learning Techniques
Photonic chip design has seen significant advancements with the adoption of inverse design methodologies, offering flexibility and efficiency in optimizing device performance. However, the black-box nature of the optimiz…
Interpretable Machine LearningUnderstanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning
Context. The outer Milky Way has a lower metallicity than our solar neighbourhood, but still many molecules are detected in the region. Molecular line ratios can serve as probes to better understand the chemistry and phy…
Interpretable Machine LearningManifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry
Modern machine learning increasingly leverages the insight that high-dimensional data often lie near low-dimensional, non-linear manifolds, an idea known as the manifold hypothesis. By explicitly modeling the geometric s…
Dimensionality ReductionInterpretable Machine LearningNavigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users
The Rashomon effect describes the observation that in machine learning (ML) multiple models often achieve similar predictive performance while explaining the underlying relationships in different ways. This observation h…
Additive modelsInterpretable Machine LearningMulti-Armed BanditsAttention Mechanisms in Dynamical Systems: A Case Study with Predator-Prey Models
Attention mechanisms are widely used in artificial intelligence to enhance performance and interpretability. In this paper, we investigate their utility in modeling classical dynamical systems -- specifically, a noisy pr…
Interpretable Machine LearningTowards Probabilistic Dynamic Security Assessment and Enhancement of Large Power Systems
This paper proposes a novel methodology for probabilistic dynamic security assessment and enhancement of power systems that considers load and generation variability, N-2 contingencies, and uncertain cascade propagation …
Interpretable Machine LearningInterpretable machine learning-guided design of Fe-based soft magnetic alloys
We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals tha…
Feature ImportanceInterpretable Machine LearningUncertainty QuantificationNFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy Forecasting
Evolving Fuzzy Systems (eFS) have gained significant attention due to their ability to adaptively update their structure in response to data dynamics while maintaining interpretability. However, the lack of publicly avai…
Interpretable Machine LearningNeurosymbolic Association Rule Mining from Tabular Data
Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimensional datasets often result in an excessive…
Interpretable Machine Learning