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http://hdl.handle.net/1942/32502
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DC Field | Value | Language |
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dc.contributor.author | CONCEPCION PEREZ, Leonardo | - |
dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | Grau, Isel | - |
dc.contributor.author | Pedrycz, Witold | - |
dc.date.accessioned | 2020-10-19T14:33:28Z | - |
dc.date.available | 2020-10-19T14:33:28Z | - |
dc.date.issued | 2020 | - |
dc.date.submitted | 2020-10-12T08:42:49Z | - |
dc.identifier.citation | IEEE Transactions on Cybernetics, 52 (5), p. 2994-3005 | - |
dc.identifier.issn | 2168-2267 | - |
dc.identifier.uri | http://hdl.handle.net/1942/32502 | - |
dc.description.abstract | Fuzzy-Rough Cognitive Networks (FRCNs) are recurrent neural networks intended for structured classification purposes in which the problem is described by an explicit set of features. The advantage of this granular neural system relies on its transparency and simplicity while being competitive to state-of-the-art classifiers. Despite of their relative empirical success in terms of prediction rates, there are limited studies on FRCNs' dynamic properties and how their building blocks contribute to algorithm's performance. In this paper, we theoretically study these issues and conclude that boundary and negative neurons always converge to a unique fixed-point attractor. Moreover, we demonstrate that negative neurons have no impact on algorithm's performance and that the ranking of positive neurons is invariant. Moved by our theoretical findings, we propose two simpler fuzzy-rough classifiers that overcome the detected issues and maintain the competitive prediction rates of this classifier. Toward the end, we present a case study concerned with image classification in which a Convolutional Neural Network is coupled with one of the simpler models derived from the theoretical analysis of the FRCN model. The numerical simulations suggest that, once the features have been extracted, our granular neural system performs as well as other recurrent neural networks. | - |
dc.description.sponsorship | The authors would like to thank the anonymous reviewers for their valuable and constructive feedback. | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.rights | 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. | - |
dc.subject.other | Index Terms-rough cognitive mapping | - |
dc.subject.other | fuzzy-rough cognitive networks | - |
dc.subject.other | convergence | - |
dc.subject.other | granular computing | - |
dc.title | Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 3005 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 2994 | - |
dc.identifier.volume | 52 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1109/TCYB.2020.3022527 | - |
dc.identifier.pmid | 33027021 | - |
dc.identifier.isi | 000798227800038 | - |
dc.identifier.eissn | 2168-2275 | - |
local.provider.type | - | |
local.uhasselt.uhpub | yes | - |
local.uhasselt.international | yes | - |
item.contributor | CONCEPCION PEREZ, Leonardo | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | Grau, Isel | - |
item.contributor | Pedrycz, Witold | - |
item.fullcitation | CONCEPCION PEREZ, Leonardo; NAPOLES RUIZ, Gonzalo; Grau, Isel & Pedrycz, Witold (2020) Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models. In: IEEE Transactions on Cybernetics, 52 (5), p. 2994-3005. | - |
item.accessRights | Restricted Access | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2023 | - |
crisitem.journal.issn | 2168-2267 | - |
crisitem.journal.eissn | 2168-2275 | - |
Appears in Collections: | Research publications |
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Fuzzy-Rough_Cognitive_Networks_Theoretical_Analysis_and_Simpler_Models.pdf Restricted Access | Published version | 817.62 kB | Adobe PDF | View/Open Request a copy |
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