arXiv:2608. 11508v1 Announce Type: new Abstract: Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints.
By Seungeun Lee, Joao Fonseca, Julia Stoyanovich
arXiv:2511.15371v3 Announce Type: replace
Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous m...
By Eddie Conti, \'Alvaro Parafita, Axel Brando
arXiv:2607. 24145v1 Announce Type: new Abstract: Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution better, or with respect to the performance on a downstream task.
By Muhammad Rajabinasab, Arthur Zimek
arXiv:2610.01641v1 Announce Type: cross
Abstract: Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive...
By Poushali Sengupta, Sabita Maharjan, Frank Eliassen, Shashi Raj Pandey, Yan Zhang
arXiv:2602. 09326v2 Announce Type: replace Abstract: Shapley values are widely used for model-agnostic data valuation and feature attribution, yet they implicitly assume contributors are interchangeable.
By Kiljae Lee, Ziqi Liu, Weijing Tang, Yuan Zhang
arXiv:2510.12734v2 Announce Type: replace
Abstract: Variable importance (VI) methods are often used for hypothesis generation, feature selection, and scientific validation. In the standard VI pipelin...
By Jon Donnelly, Srikar Katta, Emanuele Borgonovo, Cynthia Rudin