Correntropy-based density-preserving data sampling as an alternative to standard cross-validation
Authors: Budka, M. and Gabrys, B.
Journal: Proceedings of the International Joint Conference on Neural Networks
ISBN: 9781424469178
DOI: 10.1109/IJCNN.2010.5596717
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. © 2010 IEEE.
https://eprints.bournemouth.ac.uk/16572/
Source: Scopus
Correntropy-based density-preserving data sampling as an alternative to standard cross-validation
Authors: Budka, M. and Gabrys, B.
Journal: 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010
ISSN: 2161-4393
https://eprints.bournemouth.ac.uk/16572/
Source: Web of Science (Lite)
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)
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/16572/
Source: Manual
Correntropy–based density–preserving data sampling as an alternative to standard cross–validation
Authors: Budka, M. and Gabrys, B.
Pages: 1-8
Publisher: IEEE
ISBN: 978-1-4244-6916-1
DOI: 10.1109/IJCNN.2010.5596717
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/16572/
Source: Manual
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)
Dates: 18-23 July 2010
Pages: 1-8
Publisher: IEEE
ISBN: 9781424481262
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2010.5596717
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/16572/
Source: Manual
Preferred by: Marcin Budka
Correntropy-based density-preserving data sampling as an alternative to standard cross-validation.
Authors: Budka, M. and Gabrys, B.
Journal: IJCNN
Pages: 1-8
Publisher: IEEE
ISBN: 978-1-4244-6916-1
https://eprints.bournemouth.ac.uk/16572/
https://ieeexplore.ieee.org/xpl/conhome/5581822/proceeding
Source: DBLP
Correntropy–based density–preserving data sampling as an alternative to standard cross–validation
Authors: Budka, M. and Gabrys, B.
Pages: 1-8
Publisher: IEEE
ISBN: 978-1-4244-6916-1
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/16572/
Source: BURO EPrints