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http://hdl.handle.net/1942/27957
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DC Field | Value | Language |
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dc.contributor.author | BELLO GARCIA, Marilyn | - |
dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | FUENTES HERRERA, Ivett | - |
dc.contributor.author | Grau, Isel | - |
dc.contributor.author | Falcon, Rafael | - |
dc.contributor.author | Bello, Rafael | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.date.accessioned | 2019-04-02T11:16:15Z | - |
dc.date.available | 2019-04-02T11:16:15Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Uncertainty Management with Fuzzy and Rough Sets Recent Advances and Applications, SPRINGER INTERNATIONAL PUBLISHING AG,p. 317-335 | - |
dc.identifier.isbn | 9783030104627 | - |
dc.identifier.issn | 1434-9922 | - |
dc.identifier.uri | http://hdl.handle.net/1942/27957 | - |
dc.description.abstract | Rough Cognitive Ensembles (RCEs) can be defined as a multiclassifier system composed of a set of Rough Cognitive Networks (RCNs), each operating at a different granularity degree. While this model is capable of outperforming several traditional classifiers reported in the literature, there is still room for enhancing its performance. In this paper, we propose a fuzzy strategy to activate the RCN input neurons before performing the inference process. This fuzzy activation mechanism essentially quantifies the extent to which an object belongs to the intersection between its similarity class and each granular region in the RCN topology. The numerical simulations have shown that the improved ensemble classifier significantly outperforms the original RCE model for the adopted datasets. After comparing the proposed model to 14 wellknown classifiers, the experimental evidence confirms that our scheme yields very promising classification rates. | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER INTERNATIONAL PUBLISHING AG | - |
dc.relation.ispartofseries | Studies in Fuzziness and Soft Computing | - |
dc.rights | Copyright Springer Nature Switzerland AG 2019 | - |
dc.subject.other | Pattern classification; Granular Computing; Ensemble learning; Rough cognitive maps; Fuzzy Activation Mechanism | - |
dc.title | Fuzzy Activation of Rough Cognitive Ensembles Using OWA Operators | - |
dc.type | Book Section | - |
local.bibliographicCitation.conferencedate | 24-26 october 2017 | - |
local.bibliographicCitation.conferencename | International Symposium on Fuzzy and Rough Sets, ISFUROS 2017 | - |
local.bibliographicCitation.conferenceplace | Varadero, Cuba | - |
dc.identifier.epage | 335 | - |
dc.identifier.spage | 317 | - |
local.bibliographicCitation.jcat | B2 | - |
local.type.refereed | Refereed | - |
local.type.specified | Book Section | - |
local.relation.ispartofseriesnr | 377 | - |
dc.identifier.doi | 10.1007/978-3-030-10463-4_16 | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007%2F978-3-030-10463-4_16 | - |
local.bibliographicCitation.btitle | Uncertainty Management with Fuzzy and Rough Sets Recent Advances and Applications | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.contributor | BELLO GARCIA, Marilyn | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | FUENTES HERRERA, Ivett | - |
item.contributor | Grau, Isel | - |
item.contributor | Falcon, Rafael | - |
item.contributor | Bello, Rafael | - |
item.contributor | VANHOOF, Koen | - |
item.fullcitation | BELLO GARCIA, Marilyn; NAPOLES RUIZ, Gonzalo; FUENTES HERRERA, Ivett; Grau, Isel; Falcon, Rafael; Bello, Rafael & VANHOOF, Koen (2018) Fuzzy Activation of Rough Cognitive Ensembles Using OWA Operators. In: Uncertainty Management with Fuzzy and Rough Sets Recent Advances and Applications, SPRINGER INTERNATIONAL PUBLISHING AG,p. 317-335. | - |
item.validation | vabb 2020 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Fuzzy Activation of Rough Cognitive Ensembles.pdf Restricted Access | Peer-reviewed author version | 438.13 kB | Adobe PDF | View/Open Request a copy |
bello2019.pdf Restricted Access | Published version | 573.23 kB | Adobe PDF | View/Open Request a copy |
ISFUROS 2017.pdf | Proof of peer review | 260.62 kB | Adobe PDF | View/Open |
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