dc.contributor.author |
Gómez, Sergio Alejandro |
dc.contributor.author |
Chesñevar, Carlos Iván |
dc.date |
2011-06-08T08:37:25Z |
dc.date |
2011-06-08T08:37:25Z |
dc.date |
2004 |
dc.identifier |
1666-6046 (versió paper) |
dc.identifier |
1666-6038 |
dc.identifier |
http://hdl.handle.net/10459.1/41496 |
dc.identifier.uri |
http://hdl.handle.net/10459.1/41496 |
dc.description |
Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering. |
dc.language |
eng |
dc.publisher |
Iberoamerican Science & Technology Education Consortium |
dc.relation |
Reproducció del document publicat a http://journal.info.unlp.edu.ar/journal/journal10/papers/JCST-Apr04-7.pdf |
dc.relation |
Journal of Computer Science & Technology, 2004, vol. 4, núm. 1, p. 45-51 |
dc.rights |
(c) Iberoamerican Science & Technology Education Consortium, 2004 |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Machine learning |
dc.subject |
Defeasible argumentation |
dc.subject |
Neural networks |
dc.subject |
Pattern classification |
dc.subject |
Xarxes neuronals (Informàtica) |
dc.title |
Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification |
dc.type |
article |
dc.type |
publishedVersion |