  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 than maximizing over them. This yields a
tighter bound on E(FP), resulting in a feature selection criterion that has
higher sensitivity in practice and is better calibrated in terms of matching
the target E(FP). Our proposed method requires the same amount of computation
as the original stability selection algorithm, and only requires the user to
specify one input parameter, a target value for E(FP). We provide theoretical
bounds on performance, and demonstrate the method on simulations and real data
from cancer gene expression studies.
