Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/30503
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dc.contributor.authorVanloffelt, Marnick-
dc.contributor.authorNAPOLES RUIZ, Gonzalo-
dc.contributor.authorVANHOOF, Koen-
dc.date.accessioned2020-02-12T14:31:49Z-
dc.date.available2020-02-12T14:31:49Z-
dc.date.issued2019-
dc.date.submitted2020-02-10T00:00:13Z-
dc.identifier.citationProceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), p. 922-928.-
dc.identifier.isbn9781728145501-
dc.identifier.urihttp://hdl.handle.net/1942/30503-
dc.description.abstractPattern classification is a popular research field within the Machine Learning discipline. Black-box models have proven to be potent classifiers in this particular field. However, their inability to provide a transparent decision mechanism is often regarded as an undesirable feature. Fuzzy-Rough Cognitive Networks are granular classifiers that have proven competitive and effective in such tasks. In this paper, we examine the contribution of the FRCN's main building blocks, being the causal weight matrix and the activation values of the neurons, to the model's average performance. Noise injection is employed to this end. Our findings suggest that optimising the weight matrix might not be as beneficial to the model's performance as suggested in previous research. Furthermore, we found that a powerful activation of the neurons included in the model topology is crucial to performance, as expected. Further research should as such focus on finding more powerful ways to activate these neurons, rather than focus on optimising the causal weight matrix.-
dc.language.isoen-
dc.publisherIEEE-
dc.subject.otherIndex Terms-pattern classification-
dc.subject.otherfuzzy-rough sets-
dc.subject.othergranular classifiers-
dc.subject.otherfuzzy rough cognitive networks-
dc.titleFuzzy-Rough Cognitive Networks: Building Blocks and Their Contribution to Performance-
dc.typeProceedings Paper-
local.bibliographicCitation.conferencedateDecember 16-19, 2019-
local.bibliographicCitation.conferencename18th IEEE International Conference on Machine Learning and Applications-
local.bibliographicCitation.conferenceplaceFlorida, USA-
dc.identifier.epage928-
dc.identifier.spage922-
local.bibliographicCitation.jcatC1-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
dc.identifier.doi10.1109/ICMLA.2019.00159-
local.provider.typePdf-
local.bibliographicCitation.btitleProceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)-
local.uhasselt.uhpubyes-
item.fulltextWith Fulltext-
item.accessRightsRestricted Access-
item.fullcitationVanloffelt, Marnick; NAPOLES RUIZ, Gonzalo & VANHOOF, Koen (2019) Fuzzy-Rough Cognitive Networks: Building Blocks and Their Contribution to Performance. In: Proceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), p. 922-928..-
item.contributorVanloffelt, Marnick-
item.contributorNAPOLES RUIZ, Gonzalo-
item.contributorVANHOOF, Koen-
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