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arXiv AI By Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly, Arthur Zimek

Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection -- Extended Version

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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