Correntropy–based density–preserving data sampling as an alternative to standard cross–validation
Authors: Budka, M. and Gabrys, B.
Conference: World Congress on Computational Intelligence (WCCI 2010)
Pages: 1-8
Publisher: IEEE
ISBN: 9781424481262
ISSN: 1098-7576
Abstract:Estimation of the generalization ability of a predictive model is an important issue, as it indicates expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures like cross–validation (CV) or bootstrap are stochastic and thus require multiple repetitions in order to produce reliable results, which can be computationally expensive if not prohibitive. The correntropy–based Density Preserving Sampling procedure (DPS) proposed in this paper eliminates the need for repeating the error estimation procedure by dividing the available data into subsets, which are guaranteed to be representative of the input dataset. This allows to produce low variance error estimates with accuracy comparable to 10 times repeated cross–validation at a fraction of computations required by CV, which has been investigated using a set of publicly available benchmark datasets and standard classifiers.
https://eprints.bournemouth.ac.uk/21012/
Source: BURO EPrints