arXiv Paper Proposes Unsupervised Evaluation of Feature Selection

A revised arXiv preprint presents an approach for evaluating feature selection methods without relying on labels or supervised ground truth. The authors argue that existing evaluation techniques carry assumptions that limit how fairly methods can be compared, and they propose an extended framework aimed at removing those dependencies. The work targets the data mining community, where feature selection is a core preprocessing step.