caa a22 4500 445842989 CHVBK 20180317145350.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s11047-010-9182-4 doi (NATIONALLICENCE)springer-10.1007/s11047-010-9182-4 Electrostatic field framework for supervised and semi-supervised learning from incomplete data [Elektronische Daten] [Marcin Budka, Bogdan Gabrys] 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. Springer Science+Business Media B.V., 2010 Pattern classification nationallicence Deficient data nationallicence Gravity field nationallicence Electrostatic field nationallicence Incomplete data nationallicence Hybrid learning nationallicence Machine learning nationallicence Missing data nationallicence Physical phenomena nationallicence Budka Marcin Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, BH12 5BB, Fern Barrow, Poole, UK aut Gabrys Bogdan Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, BH12 5BB, Fern Barrow, Poole, UK aut Natural Computing Springer Netherlands 10/2(2011-06-01), 921-945 1567-7818 10:2<921 2011 10 11047 https://doi.org/10.1007/s11047-010-9182-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11047-010-9182-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Budka Marcin Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, BH12 5BB, Fern Barrow, Poole, UK aut NATIONALLICENCE 700 1- Gabrys Bogdan Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, BH12 5BB, Fern Barrow, Poole, UK aut NATIONALLICENCE 773 0- Natural Computing Springer Netherlands 10/2(2011-06-01), 921-945 1567-7818 10:2<921 2011 10 11047 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer