Electrostatic field classifier for deficient data
Authors: Budka, M. and Gabrys, B.
Journal: Advances in Intelligent and Soft Computing
Volume: 57
Pages: 311-318
eISSN: 1860-0794
ISSN: 1867-5662
DOI: 10.1007/978-3-540-93905-4_37
Abstract:This paper investigates the suitability of recently developed models based on the physical field phenomena for classification of incomplete datasets. An original approach to exploiting incomplete training data with missing features and labels, involving extensive use of electrostatic charge analogy has been proposed. Classification of incomplete patterns has been investigated using a local dimensionality reduction technique, which aims at exploiting all available information rather than trying to estimate the missing values. The performance of all proposed methods has been tested on a number of benchmark datasets for a wide range of missing data scenarios and compared to the performance of some standard techniques.
https://eprints.bournemouth.ac.uk/9541/
Source: Scopus
Electrostatic Field Classifier for Deficient Data
Authors: Budka, M. and Gabrys, B.
Editors: Kurzynski, M. and Wozniak, M.
Pages: 311-318
Publisher: Springer
Place of Publication: Heidelberg
ISBN: 978-3-540-93904-7
DOI: 10.1007/978-3-540-93905-4_37
Abstract:This paper investigates the suitability of recently developed models based on the physical field phenomena for classification problems with incomplete datasets. An original approach to exploiting incomplete training data with missing features and labels, involving extensive use of electrostatic charge analogy, has been proposed. Classification of incomplete patterns has been investigated using a local dimensionality reduction technique, which aims at exploiting all available information rather than trying to estimate the missing values. The performance of all proposed methods has been tested on a number of benchmark datasets for a wide range of missing data scenarios and compared to the performance of some standard techniques. Several modifications of the original electrostatic field classifier aiming at improving speed and robustness in higher dimensional spaces are also discussed.
https://eprints.bournemouth.ac.uk/9541/
http://springerlink.com/content/5148221168341945/?p=01aabf3130d945fe83aaf774c00360bb&pi=8
Source: Manual
Preferred by: Marcin Budka
Electrostatic Field Classifier for Deficient Data.
Authors: Budka, M. and Gabrys, B.
Editors: Kurzynski, M. and Wozniak, M.
Volume: 57
Pages: 311-318
Publisher: Springer
ISBN: 978-3-540-93904-7
DOI: 10.1007/978-3-540-93905-4_37
https://eprints.bournemouth.ac.uk/9541/
https://doi.org/10.1007/978-3-540-93905-4
Source: DBLP
Electrostatic Field Classifier for Deficient Data
Authors: Budka, M. and Gabrys, B.
Editors: Kurzynski, M. and Wozniak, M.
Pages: 311-318
Publisher: Springer
Place of Publication: Heidelberg
ISBN: 978-3-540-93904-7
ISSN: 1615-3871
Abstract:This paper investigates the suitability of recently developed models based on the physical field phenomena for classification problems with incomplete datasets. An original approach to exploiting incomplete training data with missing features and labels, involving extensive use of electrostatic charge analogy, has been proposed. Classification of incomplete patterns has been investigated using a local dimensionality reduction technique, which aims at exploiting all available information rather than trying to estimate the missing values. The performance of all proposed methods has been tested on a number of benchmark datasets for a wide range of missing data scenarios and compared to the performance of some standard techniques. Several modifications of the original electrostatic field classifier aiming at improving speed and robustness in higher dimensional spaces are also discussed.
https://eprints.bournemouth.ac.uk/9541/
http://springerlink.com/content/5148221168341945/?p=01aabf3130d945fe83aaf774c00360bb&pi=8
Source: BURO EPrints