比随机更差：无监督特征选择为何需要基线（扩展版）
Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection -- Extended Version
AI 导读
一项arXiv评估研究以随机特征选择为基线，检验无监督特征选择方法的实际价值。结果显示，许多最先进的无监督特征选择方法在性能和效率上都不如随机选择。作者强调新方法开发中必须以随机选择为基线，并呼吁社区开发更好的特征选择评估指标。
正文 · 原文
Abstract:Many novel unsupervised feature selection methods are proposed each year, yet their empirical evaluation is limited to supervised and unsupervised evaluation metrics computed on selected datasets, along with comparisons to existing methods. However, in the absence of an established evaluation baseline, it is difficult to determine the value added to the existing literature by each of these methods, and how effective their underlying approaches are. In this evaluation study, we are using random feature selection as a baseline for evaluating unsupervised feature selection methods. While a random selection of features might seem to be a rather weak baseline, we show in this evaluation study that many of the state-of-the-art methods in unsupervised feature selection are outperformed by random feature selection in both performance and efficiency. Accordingly, we emphasize the strict requirement of considering random feature selection as a baseline in the development process of novel unsupervised feature selection methods, and we encourage the community to develop better evaluation measures for feature selection. Both would help to ensure consistent improvement and realistic assessment.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.22973 [cs.LG] |
| (or arXiv:2605.22973v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22973 arXiv-issued DOI via DataCite |
Submission history
From: Muhammad Rajabinasab [view email]
[v1]
Thu, 21 May 2026 19:04:54 UTC (263 KB)
[v2]
Fri, 19 Jun 2026 11:43:34 UTC (264 KB)
[v3]
Thu, 29 Oct 2026 14:34:34 UTC (22,766 KB)
来源：arXiv cs.AI · arxiv.org