Publication
Distributed Text Classification With an Ensemble Kernel-Based Learning Approach
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| dc.contributor.author | Silva, Catarina | |
| dc.contributor.author | Lotric, Uros | |
| dc.contributor.author | Ribeiro, Bernardete | |
| dc.contributor.author | Dobnikar, Andrej | |
| dc.date.accessioned | 2025-11-13T18:58:48Z | |
| dc.date.available | 2025-11-13T18:58:48Z | |
| dc.date.issued | 2010 | |
| dc.description | Fonte: https://www.researchgate.net/publication/224108209_Distributed_Text_Classification_With_an_Ensemble_Kernel-Based_Learning_Approach | |
| dc.description.abstract | Constructing a single text classifier that excels in any given application is a rather inviable goal. As a result, ensemble systems are becoming an important resource, since they permit the use of simpler classifiers and the integration of different knowledge in the learning process. However, many text-classification ensemble approaches have an extremely high computational burden, which poses limitations in applications in real environments. Moreover, state-of-the-art kernel-based classifiers, such as support vector machines and relevance vector machines, demand large resources when applied to large databases. Therefore, we propose the use of a new systematic distributed ensemble framework to tackle these challenges, based on a generic deployment strategy in a cluster distributed environment. We employ a combination of both task and data decomposition of the text-classification system, based on partitioning, communication, agglomeration, and mapping to define and optimize a graph of dependent tasks. Additionally, the framework includes an ensemble system where we exploit diverse patterns of errors and gain from the synergies between the ensemble classifiers. The ensemble data partitioning strategy used is shown to improve the performance of baseline state-of-the-art kernel-based machines. The experimental results show that the performance of the proposed framework outperforms standard methods both in speed and classification. | eng |
| dc.description.sponsorship | This work was supported by the Ministry of Higher Education, Science and Technology of Slovenia, and the Ministry of Science, Technology and Higher Education of Portugal (2005–2007) under the Slovenia-Portugal Bilateral Scientific Cooperation Project. This paper was recommended by Associate Editor J. A. Keane. | |
| dc.identifier.citation | Silva, Catarina & Lotric, Uros & Dobnikar, Andrej. (2010). Distributed Text Classification With an Ensemble Kernel-Based Learning Approach. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 40. 287 - 297. DOI: https://doi.org/10.1109/TSMCC.2009.2038280. | |
| dc.identifier.doi | 10.1109/tsmcc.2009.2038280 | |
| dc.identifier.eissn | 1558-2442 | |
| dc.identifier.issn | 1094-6977 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/14614 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | IEEE Canada | |
| dc.relation.hasversion | https://ieeexplore.ieee.org/document/5398989 | |
| dc.relation.ispartof | IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) | |
| dc.rights.uri | N/A | |
| dc.subject | Distributed learning | |
| dc.subject | ensembles | |
| dc.subject | kernel-based machines | |
| dc.subject | text classification | |
| dc.title | Distributed Text Classification With an Ensemble Kernel-Based Learning Approach | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 11 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | IEEE Transactions on Systems, Man, and Cybernetics: Systems | |
| oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | |
| person.familyName | Silva | |
| person.givenName | Catarina | |
| person.identifier.ciencia-id | 1B19-3DDC-BE75 | |
| person.identifier.orcid | 0000-0002-5656-0061 | |
| relation.isAuthorOfPublication | ee28e079-5ca7-4842-9094-372c40f75c38 | |
| relation.isAuthorOfPublication.latestForDiscovery | ee28e079-5ca7-4842-9094-372c40f75c38 |
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- Constructing a single text classifier that excels in any given application is a rather inviable goal. As a result, ensemble systems are becoming an important resource, since they permit the use of simpler classifiers and the integration of different knowledge in the learning process. However, many text-classification ensemble approaches have an extremely high computational burden, which poses limitations in applications in real environments. Moreover, state-of-the-art kernel-based classifiers, such as support vector machines and relevance vector machines, demand large resources when applied to large databases. Therefore, we propose the use of a new systematic distributed ensemble framework to tackle these challenges, based on a generic deployment strategy in a cluster distributed environment. We employ a combination of both task and data decomposition of the text-classification system, based on partitioning, communication, agglomeration, and mapping to define and optimize a graph of dependent tasks. Additionally, the framework includes an ensemble system where we exploit diverse patterns of errors and gain from the synergies between the ensemble classifiers. The ensemble data partitioning strategy used is shown to improve the performance of baseline state-of-the-art kernel-based machines. The experimental results show that the performance of the proposed framework outperforms standard methods both in speed and classification.
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