SOTAVerified

Feature Importance

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–750 of 890 papers

TitleStatusHype
WheaCha: A Method for Explaining the Predictions of Models of Code—0
Website fingerprinting on early QUIC traffic—0
Continual Deterioration Prediction for Hospitalized COVID-19 Patients—0
Benchmarking Perturbation-based Saliency Maps for Explaining Atari AgentsCode0
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning—0
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling—0
Explaining the Black-box Smoothly- A Counterfactual Approach—0
Modeling Household Online Shopping Demand in the U.S.: A Machine Learning Approach and Comparative Investigation between 2009 and 2017—0
Disentangled Self-Attentive Neural Networks for Click-Through Rate PredictionCode0
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients—0
On the Discovery of Feature Importance Distribution: An Overlooked AreaCode0
Prediction of Enzyme Specificity using Protein Graph Convolutional Neural Networks—0
Democratizing Evaluation of Deep Model Interpretability through Consensus—0
Elastic Net based Feature Ranking and SelectionCode0
Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift AlgorithmCode0
Using Spatio-temporal Deep Learning for Forecasting Demand and Supply-demand Gap in Ride-hailing System with Anonymised Spatial Adjacency Information—0
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded ApproachCode0
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous SensorsCode0
Shapley values for cluster importance: How clusters of the training data affect a prediction—0
Representaciones del aprendizaje reutilizando los gradientes de la retropropagacion—0
Hate and Toxic Speech Detection in the Context of Covid-19 Pandemic using XAI: Ongoing Applied Research—0
RANCC: Rationalizing Neural Networks via Concept ClusteringCode0
A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context—0
What went wrong and when?\\ Instance-wise feature importance for time-series black-box models—0
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning—0
AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation LearningCode0
Bayesian Importance of Features (BIF)Code0
A Multilinear Sampling Algorithm to Estimate Shapley ValuesCode0
Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble—0
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations—0
Altruist: Argumentative Explanations through Local Interpretations of Predictive ModelsCode0
Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural CaseCode0
Local vs. Global interpretations for NLP—0
Explaining Neural Network Predictions for Functional Data Using Principal Component Analysis and Feature Importance—0
A data-driven approach to the forecasting of ground-level ozone concentration—0
Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks—0
Embedded methods for feature selection in neural networks—0
Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning—0
Computational analysis of pathological image enables interpretable prediction for microsatellite instability—0
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop—0
Accurate and Robust Feature Importance Estimation under Distribution Shifts—0
Explainable AI without Interpretable Model—0
A Feature Importance Analysis for Soft-Sensing-Based Predictions in a Chemical Sulphonation Process—0
Demand Forecasting in Bike-sharing Systems Based on A Multiple Spatiotemporal Fusion Network—0
Reconstructing Actions To Explain Deep Reinforcement Learning—0
Better Model Selection with a new Definition of Feature Importance—0
Captum: A unified and generic model interpretability library for PyTorchCode0
Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Breaking the Communities: Characterizing community changing users using text mining and graph machine learning on Twitter—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