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Instance and feature weighted k-nearest-neighbors algorithm
dc.contributor.author | Prat, Gabriel |
dc.contributor.author | Belanche Muñoz, Luis Antonio |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2016-12-01T10:15:32Z |
dc.date.available | 2016-12-01T10:15:32Z |
dc.date.issued | 2016 |
dc.identifier.citation | Prat, G., Belanche, Ll. Instance and feature weighted k-nearest-neighbors algorithm. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016". I6doc.com, 2016, p. 605-610. |
dc.identifier.isbn | 978-287587027-8 |
dc.identifier.uri | http://hdl.handle.net/2117/97582 |
dc.description.abstract | We present a novel method that aims at providing a more stable selection of feature subsets when variations in the training process occur. This is accomplished by using an instance-weighting process -assigning different importances to instances as a preprocessing step to a feature weighting method that is independent of the learner, and then making good use of both sets of computed weigths in a standard Nearest-Neighbours classifier. We report extensive experimentation in well-known benchmarking datasets as well as some challenging microarray gene expression problems. Our results show increases in stability for most subset sizes and most problems, without compromising prediction accuracy. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | I6doc.com |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.other | Gene expression |
dc.subject.other | Feature subset |
dc.subject.other | Feature weighting |
dc.subject.other | Instance weighting |
dc.subject.other | Microarray gene expression |
dc.subject.other | Nearest neighbour |
dc.subject.other | Pre-processing step |
dc.subject.other | Prediction accuracy |
dc.subject.other | Training process |
dc.title | Instance and feature weighted k-nearest-neighbors algorithm |
dc.type | Conference report |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-178.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 19287328 |
dc.description.version | Postprint (published version) |
local.citation.author | Prat, G.; Belanche, Ll. |
local.citation.contributor | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
local.citation.publicationName | ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016 |
local.citation.startingPage | 605 |
local.citation.endingPage | 610 |