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Feature Importance

Papers

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Showing 751–800 of 890 papers

TitleStatusHype
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach—0
Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data—0
Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis—0
Explaining Neural Network Predictions for Functional Data Using Principal Component Analysis and Feature Importance—0
Explaining the Black-box Smoothly- A Counterfactual Approach—0
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Explaining Time Series by Counterfactuals—0
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models—0
Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital—0
Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis—0
Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems—0
Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections—0
Exploring Non-Linear Effects of Built Environment on Travel Using an Integrated Machine Learning and Inferential Modeling Approach: A Three-Wave Repeated Cross-Sectional Study—0
Exploring Patterns Behind Sports—0
Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning—0
Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation—0
FACT: High-Dimensional Random Forests Inference—0
FairCanary: Rapid Continuous Explainable Fairness—0
Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates—0
FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation—0
Fair MP-BOOST: Fair and Interpretable Minipatch Boosting—0
Faking feature importance: A cautionary tale on the use of differentially-private synthetic data—0
FAST: Feature Arrangement for Semantic Transmission—0
Fault Detection for agents on power grid topology optimization: A Comprehensive analysis—0
FeatNavigator: Automatic Feature Augmentation on Tabular Data—0
Feature Analyses and Modelling of Lithium-ion Batteries Manufacturing based on Random Forest Classification—0
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning—0
Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions—0
Feature Importance: A Closer Look at Shapley Values and LOCO—0
Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness—0
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models—0
Feature Importance Estimation with Self-Attention Networks—0
Feature Importance for Time Series Data: Improving KernelSHAP—0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Feature Importance in a Deep Learning Climate Emulator—0
Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection—0
Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review—0
Feature Importance of Climate Vulnerability Indicators with Gradient Boosting across Five Global Cities—0
Feature importance recap and stacking models for forex price prediction—0
Feature importance scores and lossless feature pruning using Banzhaf power indices—0
Feature Importance versus Feature Influence and What It Signifies for Explainable AI—0
Feature Selection for Discovering Distributional Treatment Effect Modifiers—0
Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble—0
Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation—0
Federated unsupervised random forest for privacy-preserving patient stratification—0
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning—0
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods—0
Fiper: a Visual-based Explanation Combining Rules and Feature Importance—0
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning—0
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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