SOTAVerified

Feature Importance

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 890 papers

TitleStatusHype
Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical FeaturesCode0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
MvFS: Multi-view Feature Selection for Recommender SystemCode1
Calibrated Explanations for RegressionCode1
Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI—0
MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction—0
Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network—0
An interpretable deep learning method for bearing fault diagnosis—0
ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPTCode0
A coupled-mechanisms modelling framework for neurodegeneration—0
Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models—0
Feature Importance versus Feature Influence and What It Signifies for Explainable AI—0
Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A Case Study on Hemorrhagic Stroke—0
Confident Feature RankingCode0
Turning hazardous volatile matter compounds into fuel by catalytic steam reforming: An evolutionary machine learning approach—0
Feature Importance Measurement based on Decision Tree SamplingCode0
FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation—0
Beyond Single-Feature Importance with ICECREAM—0
An Evaluation of Zero-Cost Proxies -- from Neural Architecture Performance to Model Robustness—0
Explaining and visualizing black-box models through counterfactual pathsCode0
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations—0
SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features—0
Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention—0
On Formal Feature Attribution and Its ApproximationCode0
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning BenchmarksCode1
Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability ConnectionCode0
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and InterpretabilityCode1
Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligence—0
Explainability is NOT a Game—0
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods—0
Upscaling Global Hourly GPP with Temporal Fusion Transformer (TFT)—0
Retrieval of Boost Invariant Symbolic Observables via Feature Importance—0
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importanceCode0
Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes—0
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity NormalizationCode1
Calculating and Visualizing Counterfactual Feature Importance ValuesCode0
Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events ExtractionCode0
Effects of heavy metal exposure on hypertension: A machine learning modeling approach—0
Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals—0
Machine learning-based characterization of hydrochar from biomass: Implications for sustainable energy and material production—0
Unbiased Gradient Boosting Decision Tree with Unbiased Feature ImportanceCode1
Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive MapsCode0
When a CBR in Hand is Better than Twins in the Bush—0
FAST: Feature Arrangement for Semantic Transmission—0
Calibrated Explanations: with Uncertainty Information and CounterfactualsCode1
Interpreting Deep Forest through Feature Contribution and MDI Feature Importance—0
EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations—0
On Pitfalls of RemOve-And-Retrain: Data Processing Inequality PerspectiveCode0
TIGTEC : Token Importance Guided TExt Counterfactuals—0
Π-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layerCode0
Show:102550
← PrevPage 8 of 18Next →
All datasetsbostonBreastcancerDiabetesDigitsirisWine

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.76—Unverified
2Garson Variable ImportancePearson 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