Electrostatic field framework for supervised and semi-supervised learning from incomplete data

This source preferred by Marcin Budka

Authors: Budka, M. and Gabrys, B.

Journal: Natural Computing

DOI: 10.1007/s11047-010-9182-4

In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy, has been used. The framework supports a hybrid supervised and unsupervised training scenario, enabling learning simultaneously from both labelled and unlabelled data using the same set of rules and adaptation mechanisms. Classification of incomplete patterns has been facilitated by introducing a local dimensionality reduction technique, which aims at exploiting all available information using the data ‘as is’, rather than trying to estimate the missing values. The performance of all proposed methods has been extensively tested in a wide range of missing data scenarios, using a number of standard benchmark datasets in order to make the results comparable with those available in current and future literature. Several modifications to the original electrostatic field classifier aiming at improving speed and robustness in higher dimensional spaces have also been introduced and discussed.

This data was imported from DBLP:

Authors: Budka, M. and Gabrys, B.

Journal: Natural Computing

Volume: 10

Pages: 921-945

DOI: 10.1007/s11047-010-9182-4

This data was imported from Scopus:

Authors: Budka, M. and Gabrys, B.

Journal: Natural Computing

Volume: 10

Issue: 2

Pages: 921-945

eISSN: 1572-9796

ISSN: 1567-7818

DOI: 10.1007/s11047-010-9182-4

In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy, has been used. The framework supports a hybrid supervised and unsupervised training scenario, enabling learning simultaneously from both labelled and unlabelled data using the same set of rules and adaptation mechanisms. Classification of incomplete patterns has been facilitated by introducing a local dimensionality reduction technique, which aims at exploiting all available information using the data 'as is', rather than trying to estimate the missing values. The performance of all proposed methods has been extensively tested in a wide range of missing data scenarios, using a number of standard benchmark datasets in order to make the results comparable with those available in current and future literature. Several modifications to the original Electrostatic Field Classifier aiming at improving speed and robustness in higher dimensional spaces have also been introduced and discussed. © 2010 Springer Science+Business Media B.V.

This data was imported from Web of Science (Lite):

Authors: Budka, M. and Gabrys, B.

Journal: NATURAL COMPUTING

Volume: 10

Issue: 2

Pages: 921-945

ISSN: 1567-7818

DOI: 10.1007/s11047-010-9182-4

The data on this page was last updated at 04:42 on September 20, 2017.