Budka, M. and Gabrys, B., 2010. Correntropy–based density–preserving data sampling as an alternative to standard cross–validation. In: World Congress on Computational Intelligence (WCCI 2010), 18-23 July 2010, Barcelona, Spain, pp. 1-8.
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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.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||ICJNN 2010: Barcelona Spain 18-23 July 2010|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||Faculty of Science & Technology|
|Deposited By:||Dr Marcin Budka|
|Deposited On:||06 Jan 2014 16:08|
|Last Modified:||12 Apr 2016 04:15|
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