Integrated path stability selection

arXiv:2403.15877v1 Announce Type: cross
Abstract: Stability selection is a widely used method for improving the performance of feature selection algorithms. However, stability selection has been found to be highly conservative, resulting in low sensitivity. Further, the theoretical bound on the expected number of false positives, E(FP), is relatively loose, making it difficult to know how many false positives to expect in practice. In this paper, we introduce a novel method for stability selection based on integrating the stability paths rather …

abstract algorithms arxiv false false positives feature feature selection found however improving low making path performance sensitivity stability stat.me stat.ml type