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Distributed classification based on distances between probability distributions in feature space
dc.contributor.author | Montero Manso, Pablo | |
dc.contributor.author | Morán-Fernández, Laura | |
dc.contributor.author | Bolón-Canedo, Verónica | |
dc.contributor.author | Vilar, José | |
dc.contributor.author | Alonso-Betanzos, Amparo | |
dc.date.accessioned | 2024-04-01T16:21:50Z | |
dc.date.available | 2024-04-01T16:21:50Z | |
dc.date.issued | 2019-09 | |
dc.identifier.citation | Montero-Manso, P., Morán-Fernández, L., Bolón-Canedo, V., Vilar, J. A., & Alonso-Betanzos, A. (2019). Distributed classification based on distances between probability distributions in feature space. Information Sciences, 496, 431–450. https://doi.org/10.1016/j.ins.2018.12.044 | es_ES |
dc.identifier.issn | 0020-0255 | |
dc.identifier.uri | http://hdl.handle.net/2183/36030 | |
dc.description | © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Montero-Manso, P., Morán-Fernández, L., Bolón-Canedo, V., Vilar, J. A., & Alonso-Betanzos, A. (2019). ‘Distributed classification based on distances between probability distributions in feature space’ has been accepted for publication in: Information Sciences, 496, 431–450. The Version of Record is available online at https://doi.org/10.1016/j.ins.2018.12.044. | es_ES |
dc.description.abstract | [Abstract]: We consider a distributed framework where training and test samples drawn from the same distribution are available, with the training instances spread across disjoint nodes. In this setting, a novel learning algorithm based on combining with different weights the outputs of classifiers trained at each node is proposed. The weights depend on the distributional distance between each node and the test set in the feature space. Two different weighting approaches are introduced, which are referred to as per-Node Weighting (pNW) and per-Instance Weighting (pIW). While pNW assigns the same weight to all test instances at each node, pIW allows distinct weights for test instances differently represented at the node. By construction, our approach is particularly useful to deal with unbalanced nodes. Our methods require no communication between nodes, allowing for data privacy, independence of the kind of trained classifier at each node and maximum training speedup. In fact, our methods do not require retraining of the node’s classifiers if available. Although a range of different combination rules are considered to ensemble the single classifiers, theoretical support for the optimality of using the sum rule is provided. Our experiments illustrate all of these properties and show that pIW produces the highest classification accuracies compared with pNW and the standard unweighted approaches. | es_ES |
dc.description.sponsorship | This research has been supported by Spanish Ministerio de Economía y Competitividad (grants TIN2015-65069-C2-1-R, MTM2014-52876-R and MTM2017-82724-R), and by the Consellería de Industria of the Xunta de Galicia (projects GRC2014/035, Grupos de Referencia Competitiva ED431D-R2016/045 and ED431C-2016-015, and Centro Singular de Investigación de Galicia ED431G/01), all of them through the European Regional Development Fund, ERDF. | es_ES |
dc.description.sponsorship | Xunta de Galicia; GRC2014/035 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D-R2016/045 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2016-015 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-65069-C2-1-R/ES/ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA DE LA CLASIFICACION Y LA REGRESION | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRI | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.ins.2018.12.044 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Distributed classification | es_ES |
dc.subject | Distributional distances | es_ES |
dc.subject | Classifiers combination | es_ES |
dc.subject | Imbalanced data set | es_ES |
dc.subject | Classification accuracy | es_ES |
dc.title | Distributed classification based on distances between probability distributions in feature space | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Information Sciences | es_ES |
UDC.volume | 496 | es_ES |
UDC.startPage | 431 | es_ES |
UDC.endPage | 450 | es_ES |
dc.identifier.doi | 10.1016/j.ins.2018.12.044 |
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