arXiv Machine Learning
Aug 13

Towards Truly Unsupervised Evaluation of Feature Selection

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 Machine Learning
Jul 28

An Empirical Study of Feature Selection Granularity

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 Machine Learning
Aug 4

Beyond Noise: A Hypothesis Testing Approach to Robust Feature Selection

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