On Evaluation of Unsupervised Feature Selection for Pattern Classification
This addresses the evaluation reliability issue for researchers in machine learning, though it is incremental as it focuses on improving assessment rather than proposing new methods.
The paper tackled the problem of evaluating unsupervised feature selection methods by showing that using single-label datasets leads to inconsistent performance rankings, and found that adopting a multi-label framework on 21 datasets results in markedly different rankings.
Unsupervised feature selection aims to identify a compact subset of features that captures the intrinsic structure of data without supervised label. Most existing studies evaluate the performance of methods using the single-label dataset that can be instantiated by selecting a label from multi-label data while maintaining the original features. Because the chosen label can vary arbitrarily depending on the experimental setting, the superiority among compared methods can be changed with regard to which label happens to be selected. Thus, evaluating unsupervised feature selection methods based solely on single-label accuracy is unreasonable for assessing their true discriminative ability. This study revisits this evaluation paradigm by adopting a multi-label classification framework. Experiments on 21 multi-label datasets using several representative methods demonstrate that performance rankings differ markedly from those reported under single-label settings, suggesting the possibility of multi-label evaluation settings for fair and reliable comparison of unsupervised feature selection methods.