The paper argues that many newly proposed unsupervised feature selection methods are evaluated only against other methods without a proper baseline. Using random feature selection as a baseline, the authors demonstrate that several state‑of‑the‑art methods are actually outperformed in both performance and efficiency. They call for the inclusion of random selection as a baseline and for the development of better evaluation metrics to ensure realistic assessment of future methods.
By Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly, Arthur Zimek
arXiv:2511. 20851v3 Announce Type: replace-cross Abstract: Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Elimination are either computationally costly or lack a statistically justified stopping criterion for their importance scores.
By Mousam Sinha, Tirtha Sarathi Ghosh, Koushik Biswas, Ridam Pal
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:2609.07493v1 Announce Type: new
Abstract: In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying...
By Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier
arXiv:2608.12057v4 Announce Type: replace
Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of eval...
By Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek
arXiv:2608. 12057v1 Announce Type: new Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of evaluation techniques to measure the quality of a specific method.
By Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek
arXiv:2609.24126v1 Announce Type: cross
Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important fe...
By Xuhui Liu, Lili Zheng
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:2610.08345v1 Announce Type: cross
Abstract: Using a replica-symmetric high-dimensional characterization, we develop an inferential framework for sparse support vector machines when the sample s...
By Peng Zeng, Hanwen Huang
arXiv:2511.12451v2 Announce Type: replace-cross
Abstract: In high-dimensional scientific measurements, many features in a single sample often capture the same underlying physical attributes, producin...
By Abdullah Al Bashit, Prakash Nepal, Lee Makowski
arXiv:2606. 31686v1 Announce Type: cross Abstract: Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret.
By Jesus S. Aguilar-Ruiz
arXiv:2508. 14268v2 Announce Type: replace-cross Abstract: Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest.
By Chenghui Zheng, Garvesh Raskutti