Quasi-Random resamplings, with applications to rule-samplng, cross-validation and (su-)bagging
Résumé
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 section 4.
Domaines
InformatiqueOrigine | Fichiers produits par l'(les) auteur(s) |
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