Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version
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. Most of the methods commonly used for the unsupervised evaluation of feature selection algorithms suffer from critical design flaws which question their unsupervised nature. In this paper, we provide a critical discussion on the established allegedly unsupervised evaluation techniques, and shed light on the reasons why they are not truly unsupervised but, at best, supervised evaluation under an unsupervised downstream task. We also propose a novel, truly unsupervised evaluation fram...
| Authors | Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek |
|---|---|
| Primary category | cs.LG |
| Published | 12 August 2026 |
| Last updated at source | 14 September 2026 |
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