HPOSS: A hierarchical portfolio optimization stacking strategy to reduce the generalization error of ensembles of models.

ABSTRACT: Surrogate models are frequently used to replace costly engineering simulations. A single surrogate is frequently chosen based on previous experience or by fitting multiple surrogates and selecting one based on mean cross-validation errors. A novel stacking strategy will be presented in this paper. This new strategy results from reinterpreting the model selection process based on the generalization error. For the first time, this problem is proposed to be translated into a well-studied financial problem: portfolio management and optimization. In short, it is demonstrated that the individual residues calculated by leave-one-out procedures are samples from a given random variable ϵi, whose second non-central moment is the i-th model's generalization error. Thus, a stacking methodology based s

SUBMITTER: Ozelim LCSM 

PROVIDER: S-EPMC10470931 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature