The paper develops an explanatory framework for weighted naive Bayes classifiers by transforming the traditional supervised distance based on conditional log-likelihoods into a discriminative log‑odds representation. It demonstrates that this new representation yields a distance identical to the α1 distance between vectors of analytical Shapley values, thereby linking model geometry to local explanations. The authors also empirically evaluate various supervised distances derived from these representations using a k‑nearest neighbors classifier.
By Vincent Lemaire, Fabrice Cl\'erot
The paper introduces an imprecise Dirichlet model (IDM) approach to evaluate the robustness of Shapley values in tree-based models such as decision trees and random forests. By quantifying interval-valued Shapley values—using pessimistic and averaging principles—when a few unannotated instances are added to tree leaves, the authors derive efficient computational methods and extend the framework to Banzhaf values. Experiments and case studies demonstrate how these interval-valued explanations can be applied to debias uninformative features.
By Chenrui Zhu, Vu-Linh Nguyen, Marie-H\'el\`ene Masson, S\'ebastien Destercke
arXiv:2607. 03675v1 Announce Type: new Abstract: Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set.
By Yinan Shen, Ziao Yang, Hongfu Liu
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.
By Paul-Gauthier No\'e, Miquel Perell\'o-Nieto, Jean-Fran\c{c}ois Bonastre, Peter Flach
arXiv:2507. 07156v2 Announce Type: replace-cross Abstract: Supervised machine learning pipelines trained on features derived from persistent homology have been experimentally observed to ignore much of the information contained in a persistence diagram.
By Nicole Abreu, Parker B. Edwards, Francis Motta
arXiv:2410. 01917v3 Announce Type: replace-cross Abstract: Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predictions to specific input features.
By Christopher Musco, R. Teal Witter
The paper introduces weighted t‑SNE, a variant of t‑SNE that incorporates feature importance scores from feature scorers into the 2‑D embedding of high‑dimensional data. It explains how to implement the method, proposes using the silhouette coefficient to evaluate the resulting visualizations, and demonstrates its use on synthetic and real‑world tabular datasets with nine different feature scorers. The approach allows practitioners to compare feature scorers visually and to interpret how machine‑learning models organize data based on learned feature importance.
By Bruno Iochins Grisci, Mario Inostroza-Ponta, M\'arcio Dorn
arXiv:2607. 01010v1 Announce Type: cross Abstract: The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend to exhibit, despite their high-dimensional representation.
By Konstantin H\"aberle, Helmut B\"olcskei
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
The paper introduces LIDFS, an unsupervised feature selection technique that uses Local Intrinsic Dimensionality (LID) to rank features based on their local discriminability. By treating each feature as a point and estimating its LID via maximum likelihood from distances to its k nearest neighboring features, LIDFS assigns higher importance to features with lower LID and refines the ranking to remove locally redundant features. Experiments on 23 high‑dimensional datasets show that LIDFS outperforms both classic and state‑of‑the‑art methods in supervised and clustering evaluations while running faster than its closest competitor.
By Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi, Muhammad Rajabinasab, Arthur Zimek
arXiv:2602. 10441v2 Announce Type: replace Abstract: Data lakes have become a fundamental platform for large-scale machine learning by enabling flexible management of heterogeneous data.
By Feiyu Pan, Tianbin Zhang, Aoqian Zhang, Yu Sun, Zheng Wang, Lixing Chen, Li Pan, Jianhua Li
arXiv:2607. 25680v1 Announce Type: cross Abstract: We propose Rashomon Alignment (RA), a new measure to assess functional similarity between two models.
By Mois\'es Santos, Peter van der Putten, Bernhard Pfahringer, Carlos Soares