Quasi-Random Resamplings, with aplications to rule Extraction, Cross-Validation and (su-)bagging
Olivier Teytaud, Justin Bedo, Stéphane Lallich and Elie Prudhomme
In: IIIA'06, 2006, Helsinki.
Resampling (typically, but not necessarily, bootstrapping) is a well-known
stochastic technique for improving estimates in particular for small samples. It
is known very eﬃcient in many cases. Its drawback is that resampling leads to a
compromise computational cost / stability through the number of resamplings.
The computational cost is due to the study of multiple randomly drawn resam-
ples. Intuitively, we want some more properly distributed resamples to improve
the stability of resampling-based algorithms. Quasi-random numbers are a well-
known technique for improving the convergence rate of data-based estimates.
We here consider quasi-random version of resamplings. We apply this technique
to BSFD, a data-mining algorithm for simultaneous-hypothesis-testing, to cross-
validation, and to (su-)bagging, an ensemble method for learning. We present
quasi-random numbers in section 2. We present bootstrap and a quasi-random
version of bootstrap-sampling in section 3. We present experimental results in