2026-05-06T18:52:07Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/42212021-11-10T09:38:16Zcom_10259_5377com_10259_5086com_10259_2604com_10259_4219col_10259_5378col_10259_4220

Arnaiz González, Álvar 39 500 0000-0001-6965-0237 Diez Pastor, José Francisco 156 500 Rodríguez Diez, Juan José 477 500 García Osorio, César 212 500 0000-0002-1206-1084 2016-09-01T09:42:59Z 2016-09-01T09:42:59Z 2016-09 0950-7051 http://hdl.handle.net/10259/4221 10.1016/j.knosys.2016.05.056 Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems with very large-to-massive data sets. In this paper, two new algorithms with linear complexity for instance selection purposes are presented. Both algorithms use locality-sensitive hashing to find similarities between instances. While the complexity of conventional methods (usually quadratic, O(n2), or log-linear, O(nlogn)) means that they are unable to process large-sized data sets, the new proposal shows competitive results in terms of accuracy. Even more remarkably, it shortens execution time, as the proposal manages to reduce complexity and make it linear with respect to the data set size. The new proposal has been compared with some of the best known instance selection methods for testing and has also been evaluated on large data sets (up to a million instances). Supported by the Research Projects TIN 2011-24046 and TIN 2015-67534-P from the Spanish Ministry of Economy and Competitiveness. application/pdf eng Elsevier Knowledge-Based Systems. 2016. V. 107, p. 83–95 http://dx.doi.org/10.1016/j.knosys.2016.05.056 info:eu-repo/grantAgreement/MINECO/TIN 2011-24046 info:eu-repo/grantAgreement/MINECO/TIN 2015-67534-P Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Nearest neighbor Data reduction Instance selection Hashing Big data Informática Computer science Instance selection of linear complexity for big data info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/4221/8/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 8 THUMBNAIL Arnaiz-KBS_2016.pdf.jpg Arnaiz-KBS_2016.pdf.jpg IM Thumbnail image/jpeg 3919 https://riubu.ubu.es/bitstream/10259/4221/7/Arnaiz-KBS_2016.pdf.jpg e618d1f61323a2ed3cc538ca8ff0c262 MD5 7 ORIGINAL Arnaiz-KBS_2016.pdf Arnaiz-KBS_2016.pdf application/pdf 1184745 https://riubu.ubu.es/bitstream/10259/4221/1/Arnaiz-KBS_2016.pdf d4e42af8a5936dad7b8f6e543e96d24f MD5 1 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/4221/5/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 5 TEXT Arnaiz-KBS_2016.pdf.txt Arnaiz-KBS_2016.pdf.txt Extracted text text/plain 67394 https://riubu.ubu.es/bitstream/10259/4221/6/Arnaiz-KBS_2016.pdf.txt 6389f85d4eac645f34bbf67d56f6eb1a MD5 6 10259/4221 oai:riubu.ubu.es:10259/4221 2021-11-10 10:38:16.955 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es RWwgYXV0b3IgY29tbyDDum5pY28gdGl0dWxhciBkZSBsb3MgZGVyZWNob3MgZGUgcHJvcGllZGFkIGludGVsZWN0dWFsIGRlIGxhIG9icmEsIG8gZGlzcG9uaWVuZG8gZGUgbG9zIGRlYmlkb3MgcGVybWlzb3MgZGUgbG9zIG90cm9zIHRpdHVsYXJlcywgc2kgbG9zIGh1YmllcmEsIHkgZW4gdmlydHVkIGRlIGxvcyBkZXJlY2hvcyBxdWUgbGUgY29uZmllcmUgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3IsIApBVVRPUklaQSBhIGxhIFVuaXZlcnNpZGFkIGRlIEJ1cmdvcyBhIGRpZnVuZGlyLCBkZSBtYW5lcmEgZ3JhdHVpdGEsIGVsIGNvbnRlbmlkbyBkZSBsb3MgYXJjaGl2b3MgZGlnaXRhbGVzIHF1ZSBjb3JyZXNwb25kZW4gYWwgZG9jdW1lbnRvIGRlc2NyaXRvIGFudGVyaW9ybWVudGUsIGNvbiBjYXLDoWN0ZXIgbm8gZXhjbHVzaXZvIHkgZGUgbWFuZXJhIHDDumJsaWNhIGVuIGFjY2VzbyBhYmllcnRvIGEgdHJhdsOpcyBkZSBJbnRlcm5ldCwgcGFyYSBsbyBxdWUgbGEgQmlibGlvdGVjYSBwcm9jZWRlcsOhIGEgYXJjaGl2YXJsb3MgZW4gZWwgUmVwb3NpdG9yaW8gSW5zdGl0dWNpb25hbC4gQXNpbWlzbW8gYXV0b3JpemEgYSBsYSBVbml2ZXJzaWRhZCBkZSBCdXJnb3MgYSByZWFsaXphciBsYXMgdHJhbnNmb3JtYWNpb25lcyBuZWNlc2FyaWFzIGRlIGZvcm1hdG8sIG5vIGRlIGNvbnRlbmlkbywgcGFyYSBnYXJhbnRpemFyIGxhIHByZXNlcnZhY2nDs24geSBlbCBhY2Nlc28gZW4gZWwgZnV0dXJvLgoKRWwgYXV0b3IgZGlzcG9uZSwgZW4gdG9kbyBjYXNvLCBkZWwgZGVyZWNobyBhIHJldm9jYXIgZXN0YSBhdXRvcml6YWNpw7NuLgoKTGEgY2VzacOzbiBkZSBkZXJlY2hvcyBkZSBlc3RhIG9icmEgc2UgZW5jdWVudHJhIHN1amV0YSBhIGxhIGxlZ2lzbGFjacOzbiB2aWdlbnRlIHNvYnJlIHByb3BpZWRhZCBpbnRlbGVjdHVhbCB5IGRlcmVjaG9zIGRlIGF1dG9yLiBTdSBkaWZ1c2nDs24gZW4gZWwgUmVwb3NpdG9yaW8gc2Vyw6EgYmFqbyBsYSBtb2RhbGlkYWQgZGUgbGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyBvIGVxdWl2YWxlbnRlOiByZWNvbm9jaW1pZW50byDigJMgdXNvIG5vIGNvbWVyY2lhbCDigJMgc2luIG9icmEgZGVyaXZhZGEsIHBvciBsYSBxdWUgc2UgcGVybWl0ZSBoYWNlciBjb3BpYSwgZGlzdHJpYnVpciB5IGNvbXVuaWNhciBww7pibGljYW1lbnRlIGxhIG9icmEgc2llbXByZSBxdWUgc2UgY2l0ZSBhbCBhdXRvciwgZWwgdXNvIHF1ZSBzZSBoYWdhIGRlIGVsbGEgc2VhIG5vIGNvbWVyY2lhbCB5IG5vIHNlIGNyZWVuIG9icmFzIGRlcml2YWRhcyBhIHBhcnRpciBkZSBsYSBvcmlnaW5hbC4K