An Empirical Study of Feature Selection Granularity
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. Existing research in this area has largely focused on developing novel algorithms (in both supervised and unsupervised settings), proposing new evaluation metrics and frameworks, or benchmarking the performance of existing methods. In this work, we examine feature selection through an algorithmic design perspective. Conventional feature selection algorithms typically compute feature importance scores globally across the entire feature set and then selec...
| Authors | Muhammad Rajabinasab, Arthur Zimek |
|---|---|
| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 23 August 2026 |
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