arXiv Machine Learning
Jul 28
Variable Importance Identification Through Lazy Training for Binary Classification
arXiv:2607. 22979v1 Announce Type: cross Abstract: Deep neural networks have been widely used in many applications (e.
Conditional Local Importance by Quantile Expectations
arXiv:2411. 08821v4 Announce Type: replace-cross Abstract: Global variable importance measures are commonly used to interpret the results of machine learning models.
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arXiv AI
Jul 21
K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data
arXiv:2607. 16478v1 Announce Type: cross Abstract: Oversampling is widely used to address class imbalance in tabular classification, but existing methods can distort the feature importance ranking underlying model explanations.
arXiv Machine Learning
Jun 10
Correcting Variable Importance Scored by Random Forests
arXiv:2606. 10770v1 Announce Type: cross Abstract: Variable importance produced by Random Forests (RF) is used widely in statistical data analysis, and has played an important role in a variety of tasks such as assisting model interpretation, model selection and diagnosis, and cost-bounded learning etc.
arXiv Machine Learning
Jul 21
MinShap: A Shapley-Based Framework for Feature Redundancy
arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.
arXiv Machine Learning
Jun 11
Provable Recovery of Locally Important Signed Features and Interactions from Random Forest
arXiv:2512. 11081v2 Announce Type: replace-cross Abstract: Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their interactions in complex prediction models.
arXiv Machine Learning
Jul 10
Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection
arXiv:2603. 23318v2 Announce Type: replace Abstract: Among the different possible strategies for evaluating the reliability of individual predictions of classifiers, robustness quantification stands out as a method that evaluates how much uncertainty a classifier could cope with before changing its prediction.
arXiv Machine Learning
Jun 4
Explaining a probabilistic prediction on the simplex with Shapley compositions
arXiv:2408. 01382v3 Announce Type: replace Abstract: Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of each feature's value to the prediction.
arXiv Machine Learning
Jul 27
A Leakage-Free Stacked Ensemble Method for Multiclass Classification
arXiv:2607. 22081v1 Announce Type: new Abstract: Multiclass classification is a fundamental problem across a wide range of domains.
arXiv AI
Aug 11
Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification
arXiv:2604. 11613v4 Announce Type: replace-cross Abstract: Transformers can perform in-context classification from a few labeled examples, yet the inference-time algorithm remains opaque.
arXiv AI
Jul 16
NodeImport: Imbalanced Node Classification with Node Importance Assessment
arXiv:2607. 13837v1 Announce Type: cross Abstract: In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resulting in biased model performance.
arXiv Machine Learning
Jun 2
Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
arXiv:2606. 00265v1 Announce Type: cross Abstract: We study quantile regression in an extrapolation regime where the covariate takes unusually large values.
arXiv Machine Learning
Jul 31
What Is The Performance Ceiling of My Classifier? Utilizing Category-Wise Influence Functions for Pareto Frontier Analysis
arXiv:2510. 03950v2 Announce Type: replace Abstract: Data-centric learning seeks to improve model performance from the perspective of data quality, and has been drawing increasing attention in the machine learning community.