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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.

Available Versions of this Item

• Correntropy–based density–preserving data sampling as an alternative to standard cross–validation. (deposited 05 Mar 2010 22:12)
  • Correntropy–based density–preserving data sampling as an alternative to standard cross–validation. (deposited 21 Oct 2010 19:13)
    • Correntropy–based density–preserving data sampling as an alternative to standard cross–validation. (deposited 17 May 2012 16:15) [Currently Displayed]

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