SOTAVerified

Feature Importance

Papers

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Showing 601–650 of 890 papers

TitleStatusHype
An exploration of features to improve the generalisability of fake news detection models—0
An interpretable deep learning method for bearing fault diagnosis—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
A Notion of Feature Importance by Decorrelation and Detection of Trends by Random Forest Regression—0
A novel feature selection framework for incomplete data—0
A Novel Hybrid Feature Importance and Feature Interaction Detection Framework for Predictive Optimization in Industry 4.0 Applications—0
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography—0
Anytime Approximate Formal Feature Attribution—0
A Pipeline for Data-Driven Learning of Topological Features with Applications to Protein Stability Prediction—0
Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified—0
Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder—0
Area of interest adaption using feature importance—0
Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting—0
Asian Giant Hornet Control based on Image Processing and Biological Dispersal—0
Assessing high-order effects in feature importance via predictability decomposition—0
Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading—0
Assessing the Reliability of Visual Explanations of Deep Models with Adversarial Perturbations—0
Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM—0
A Survey on the Robustness of Feature Importance and Counterfactual Explanations—0
Attention cannot be an Explanation—0
Augmented Functional Random Forests: Classifier Construction and Unbiased Functional Principal Components Importance through Ad-Hoc Conditional Permutations—0
Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis—0
Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency—0
Automatic Componentwise Boosting: An Interpretable AutoML System—0
Automatic prediction of mortality in patients with mental illness using electronic health records—0
A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization—0
Axiomatic Aggregations of Abductive Explanations—0
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability—0
Benchmarking the Fidelity and Utility of Synthetic Relational Data—0
Better Model Selection with a new Definition of Feature Importance—0
Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI—0
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics—0
Beyond Single-Feature Importance with ICECREAM—0
Bias Reducing Multitask Learning on Mental Health Prediction—0
BiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging—0
BMMDetect: A Multimodal Deep Learning Framework for Comprehensive Biomedical Misconduct Detection—0
Building effective deep neural network architectures one feature at a time—0
Building effective deep neural networks one feature at a time—0
Building Matters: Spatial Variability in Machine Learning Based Thermal Comfort Prediction in Winters—0
Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability—0
CAGE: Causality-Aware Shapley Value for Global Explanations—0
Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A Case Study on Hemorrhagic Stroke—0
Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory—0
CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors—0
Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records—0
Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI—0
CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection—0
CGS-Mask: Making Time Series Predictions Intuitive for All—0
Channel Pruning Guided by Classification Loss and Feature Importance—0
Characterizing climate pathways using feature importance on echo state networks—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