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Feature Importance

Papers

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Showing 301–350 of 890 papers

TitleStatusHype
Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importanceCode0
Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories—0
XAI-Guided Enhancement of Vegetation Indices for Crop Mapping—0
Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing—0
BrainMetDetect: Predicting Primary Tumor from Brain Metastasis MRI Data Using Radiomic Features and Machine Learning AlgorithmsCode0
DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification—0
ShapG: new feature importance method based on the Shapley valueCode0
Explainability of Machine Learning Models under Missing DataCode0
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI—0
Predicting the duration of traffic incidents for Sydney greater metropolitan area using machine learning methodsCode0
The Impact of Feature Representation on the Accuracy of Photonic Neural NetworksCode0
Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis—0
Fault Detection for agents on power grid topology optimization: A Comprehensive analysis—0
Privacy Implications of Explainable AI in Data-Driven Systems—0
Multi-level Phenotypic Models of Cardiovascular Disease and Obstructive Sleep Apnea Comorbidities: A Longitudinal Wisconsin Sleep Cohort Study—0
Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks—0
Multi-LLM QA with Embodied Exploration—0
FeatNavigator: Automatic Feature Augmentation on Tabular Data—0
Deep reinforcement learning with positional context for intraday trading—0
Learned Feature Importance Scores for Automated Feature Engineering—0
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models—0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space—0
Unified Explanations in Machine Learning Models: A Perturbation ApproachCode0
Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous CatalysisCode0
I Bet You Did Not Mean That: Testing Semantic Importance via BettingCode0
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model—0
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for TransformersCode0
From SHAP Scores to Feature Importance Scores—0
Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis—0
Analyze Additive and Interaction Effects via Collaborative Trees—0
Mitigating Text Toxicity with Counterfactual Generation—0
Feature Importance and Explainability in Quantum Machine LearningCode0
Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes—0
Estimate the building height at a 10-meter resolution based on Sentinel data—0
Feature importance to explain multimodal prediction models. A clinical use caseCode0
DTization: A New Method for Supervised Feature Scaling—0
Accurate and fast anomaly detection in industrial processes and IoT environments—0
Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment—0
SIDEs: Separating Idealization from Deceptive Explanations in xAI—0
Fiper: a Visual-based Explanation Combining Rules and Feature Importance—0
MISLEAD: Manipulating Importance of Selected features for Learning Epsilon in Evasion Attack Deception—0
Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory—0
A Guide to Feature Importance Methods for Scientific InferenceCode0
Explainable AI for Fair Sepsis Mortality Predictive Model—0
Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data—0
Explainable Machine Learning System for Predicting Chronic Kidney Disease in High-Risk Cardiovascular Patients—0
CAGE: Causality-Aware Shapley Value for Global Explanations—0
Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models—0
Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision dataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Garson Variable ImportancePearson Correlation0.76—Unverified
2VarImpVIANNPearson Correlation0.76—Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.6—Unverified
2Garson Variable ImportancePearson Correlation0.22—Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.86—Unverified
2Garson Variable ImportancePearson Correlation0.64—Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.83—Unverified
2Garson Variable ImportancePearson Correlation0.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.9—Unverified
2Garson Variable ImportancePearson Correlation0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1Garson Variable ImportancePearson Correlation0.74—Unverified
2VarImpVIANNPearson Correlation0.41—Unverified