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Feature Importance

Papers

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Showing 601–650 of 890 papers

TitleStatusHype
Ranking Feature-Block Importance in Artificial Multiblock Neural NetworksCode0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Automatic Componentwise Boosting: An Interpretable AutoML System—0
Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection—0
Model Explanations via the Axiomatic Causal LensCode0
Optimizing model-agnostic Random Subspace ensemblesCode0
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the CloudCode1
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process—0
Inferring feature importance with uncertainties in high-dimensional dataCode0
Discretized Integrated Gradients for Explaining Language ModelsCode1
Feature Importance in a Deep Learning Climate Emulator—0
Data-driven advice for interpreting local and global model predictions in bioinformatics problems—0
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition—0
Improved Feature Importance Computations for Tree Models: Shapley vs. Banzhaf—0
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud DetectionCode0
Temporal Dependencies in Feature Importance for Time Series PredictionsCode1
Feature Importance-aware Transferable Adversarial AttacksCode1
Joint Shapley values: a measure of joint feature importanceCode1
Explainable Debugger for Black-box Machine Learning ModelsCode0
One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Images—0
Feature importance recap and stacking models for forex price prediction—0
ControlBurn: Feature Selection by Sparse ForestsCode1
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Interpreting Criminal Charge Prediction and Its Algorithmic Bias via Quantum-Inspired Complex Valued Networks—0
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles—0
Neural network interpretability for forecasting of aggregated renewable generationCode0
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)Code0
FairCanary: Rapid Continuous Explainable Fairness—0
Predicting Knowledge Gain during Web Search based on Multimedia Resource Consumption—0
Explaining Time Series Predictions with Dynamic MasksCode1
Evaluating Local Explanations using White-box Models—0
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance ExplanationsCode1
A Methodology for Exploring Deep Convolutional Features in Relation to Hand-Crafted Features with an Application to Music Audio ModelingCode0
Evaluating the Correctness of Explainable AI Algorithms for Classification—0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
Is Gender "In-the-Wild" Inference Really a Solved Problem?Code0
LFI-CAM: Learning Feature Importance for Better Visual ExplanationCode1
Learning Bermudans—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation—0
LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification—0
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
Grouped Feature Importance and Combined Features Effect PlotCode1
Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation—0
Text Guide: Improving the quality of long text classification by a text selection method based on feature importanceCode1
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer InformationCode0
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML—0
Transforming Feature Space to Interpret Machine Learning ModelsCode0
Hollow-tree Super: a directional and scalable approach for feature importance in boosted tree models—0
Online Feature Screening for Data Streams with Concept Drift—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