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dc.contributor.author
Rodriguez, Juan Manuel  
dc.contributor.author
Godoy, Daniela Lis  
dc.contributor.author
Mateos Diaz, Cristian Maximiliano  
dc.contributor.author
Zunino Suarez, Alejandro Octavio  
dc.date.available
2018-09-06T19:44:50Z  
dc.date.issued
2017-03  
dc.identifier.citation
Rodriguez, Juan Manuel; Godoy, Daniela Lis; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio; A multi-core computing approach for large-scale multi-label classification; IOS Press; Intelligent Data Analysis; 21; 2; 3-2017; 329-352  
dc.identifier.issn
1088-467X  
dc.identifier.uri
http://hdl.handle.net/11336/58609  
dc.description.abstract
Large scale multi-label learning, i.e. the problem of determining the associated set of labels for an instance, is gaining relevance in recent years due to the emergence of several real-world applications. Most notably, the exponential growth of the Social Web where a resource can be labeled by millions of users using one or more tags, i.e. a resource can be associated to several labels at the same time. A well-known approach for multi-label classification is the Binary Relevance (BR) algorithm which trains a binary classifier for each label independently. However, the serial implementation of BR is not suitable for medium or large datasets due to the time and computational resources required for training. For example, training classifiers for mid-size datasets using MULAN implementation of BR might take several weeks. This paper discusses a parallel implementation of the MULAN BR technique that harnesses the computational power of nowadays multi-core processors. Our implementation presents a speed-up in the training phase of up to 12 times when compared to the original MULAN implementation. In addition, the cross-validation technique of MULAN had huge RAM requirements, making it unusable with large datasets. Therefore, we have overcome this limitation by using compact data structures and taking advantage of disk caching. We have also compared our implementation against scikit-learn, a popular tool for data mining and data analysis, showing significant improvements in speed-up.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
IOS Press  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Binary Relevance Classification  
dc.subject
Multi-Core Programming  
dc.subject
Multi-Label Classification  
dc.subject
Parallel Classification  
dc.subject.classification
Ciencias de la Computación  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
A multi-core computing approach for large-scale multi-label classification  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2018-09-05T16:11:54Z  
dc.journal.volume
21  
dc.journal.number
2  
dc.journal.pagination
329-352  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina  
dc.description.fil
Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina  
dc.description.fil
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina  
dc.description.fil
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina  
dc.journal.title
Intelligent Data Analysis  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://content.iospress.com/articles/intelligent-data-analysis/ida150375  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3233/IDA-150375