Papers Feature Importance
“Feature Importance” 태그가 달린 논문 1,170편 · 필터 해제
A unified framework for global and local interpretability using adaptive derivative-ordered random explanation
The interpretability of complex machine learning models is of paramount importance, especially in real-world high-stakes domains such as healthcare and finance. However, existing post-hoc interpretability methods suffer …
Computational EfficiencyFeature ImportanceAn Explainable Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Using Molecular Descriptors
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within …
Hyperparameter OptimizationFeature ImportanceTiming-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation
Repurchase recommenders in e-commerce are commonly framed as a binary question asking "will this customer buy this item within W days", a formulation that requires a separately trained model for every horizon of interest…
Feature ImportanceActionable CBFI: Integrating Structural Decomposition and Causal Counterfactual Recourse for Tabular Machine Learning
Explainable artificial intelligence (XAI) increasingly calls for actionable counterfactual recourse, yet current methodologies face challenges related to causal invalidity, excessive cognitive burden, and predictive fail…
Feature ImportanceEnergy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials
To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographical regions with varying operating conditions. As these conditions str…
Feature ImportanceSAGE-XGBoost: Spatially Augmented Graph Embeddings--Machine Learning Framework for Natural Hazards Susceptibility Mapping under Data Scarcity
Natural hazard susceptibility mapping is often constrained by limited labeled data, reducing the generalizability of conventional machine learning and limiting the applicability of complex deep learning models. This stud…
Representation LearningFeature ImportanceData AugmentationCan Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach
Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the level of pilot control behavior. Existing explain…
Feature EngineeringFeature ImportanceFew-Shot LearningSpatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors
Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South whe…
Feature ImportanceAutomating Learner Assessment: Benchmarking Machine Learning and Deep Learning Models for EEG-Based Familiarity Prediction
Objective assessment of learning remains a fundamental challenge in education. Electroencephalography (EEG) provides a direct, non-invasive window into the neural correlates of knowledge acquisition, including cognitive …
Feature ImportanceAdaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification
Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple base learner. However, existing approache…
Feature ImportanceOn the global feature importance for interpretable and trustworthy heat demand forecasting
The paper introduces the ante-hoc Explainable AI methodology to assess the global feature importance of the Machine Learning models used for heat demand forecasting in intelligent control of District Heating Systems, wit…
Feature ImportanceMulti-perspective Imbalance-Conscious 6G Beamforming Optimization and Performance
The study presents a systematic machine learning (ML) study of 6G-IoT beamforming optimization (6GBO) using supervised and unsupervised approaches. We compared the predictive power of network, environmental, device, and …
Reinforcement LearningFeature ImportanceSPEAR: Structure Property Explainability with Attention Regularization
Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predicti…
Feature ImportanceHybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems
Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting. This study developed a hybrid machine learning framework for herd level cattle weight forecasting using automa…
Feature ImportanceBeyond Feature Importance: A Comparative Analysis of Pattern Detection Methods in Cluster Interpretation
Interpreting clustering outcomes remains a fundamental challenge in data analysis, particularly in domains such as healthcare where meaningful patterns must be extracted from high-dimensional data. While numerous explain…
Feature ImportanceExplainable Hybrid Feature Selection for Intrusion Detection in Internet of Medical Things Environments
Internet of Medical Things (IoMT) networks are hard to protect: devices are heterogeneous, computing resources are scarce, and traffic must be analyzed in real time. We present an intrusion detection system that addresse…
Intrusion DetectionFeature ImportanceBeyond Sentiment: Structured Information Extraction from Financial News
Financial sentiment analysis has become a standard component in news-driven stock prediction, yet it reduces rich, multi-dimensional news articles to a single polarity score. We hypothesize that financial news encodes mu…
Information ExtractionSentiment AnalysisFeature ImportanceA Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings
Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We present a novel cross-lingual analysis of entrainment in Mandarin-English, Hind…
Feature ImportanceTopological Data Analysis and Graph-Theoretic Approaches for Tennis Match Prediction
We present two approaches for predicting tennis match outcomes using topological data analysis and graph theory on ATP singles matches from 2000-2025. The first method applies lower-star filtration to player competitive …
Feature ImportanceScaling Time Series Classification via XAI-Driven Data Reduction
Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for downstream tasks remains under-explored. This paper bridges this gap by introduc…
Time Series ClassificationTime Series AnalysisFeature Importance