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An emotional learning-inspired ensemble classifier (ELiEC)
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
2013 (engelsk)Inngår i: Proceedings of the 2013 Federated Conference on Computer Science and Information Systems (FedCSIS) / [ed] M. Ganzha, L. Maciaszek & M. Paprzycki, Los Alamitos, CA: IEEE Computer Society, 2013, s. 137-141, artikkel-id 6643988Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper, we suggest an inspired architecture by brain emotional processing for classification applications. The architecture is a type of ensemble classifier and is referred to as 'emotional learning-inspired ensemble classifier' (ELiEC). In this paper, we suggest the weighted k-nearest neighbor classifier as the basic classifier of ELiEC. We evaluate the ELiEC's performance by classifying some benchmark datasets. © 2013 Polish Information Processing Society.

sted, utgiver, år, opplag, sider
Los Alamitos, CA: IEEE Computer Society, 2013. s. 137-141, artikkel-id 6643988
Serie
Close Nearby librariesto 300 04 Halmstad University LibraryHalmstad SE-30118, Sweden < 1 m / km FOU-EnhetenFalkenberg 311 22, Sweden22m / 34.3km Sjukhuset I VarbergVarberg 43281, Sweden38m / 60.9km Helsingborg, City Library ofHelsingborg 25225, Sweden44m / 70.6km Helsingør Municipal LibrariesHelsingor DK3000, Denmark45m / 72.1km Den Internationale Højskole, BiblioteketHelsingør DK-3000, Denmark46m / 73.4km Helsingør Gymnasium, StudiecentretHelsingør DK-3000, Denmark46m / 73.9km Studiecentret, Espergærde Gymnasium og HFEspergærde DK-3060, Denmark49m / 77.4km Sjukhuset L Hasselholm Medical BibliogHassleholm 281 25, Sweden50m / 79.9km Fredensborg BibliotekerneFredensborg DK-3480, Denmark51m / 82.0km Find more libraries » Librarian? Claim your library Federated Conference on Computer Science and Information Systems : [proceedings], ISSN 2325-0348
Emneord [en]
brain, learning (artificial intelligence), pattern classification, ELiEC, brain emotional processing, emotional learning-inspired ensemble classifier, weighted k-nearest neighbor classifier, Accuracy, Benchmark testing, Brain models, Data models, Iris, Training data
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-25466ISI: 000347171500021Scopus ID: 2-s2.0-84892547009ISBN: 978-83-60810-52-1 (digital)ISBN: 978-1-4673-4471-5 (tryckt)ISBN: 978-83-60810-53-8 (digital)OAI: oai:DiVA.org:hh-25466DiVA, id: diva2:720689
Konferanse
2013 Federated Conference on Computer Science and Information Systems (FedCSIS), Krakow, Poland, 8-11 September 2013
Tilgjengelig fra: 2014-06-02 Laget: 2014-06-02 Sist oppdatert: 2018-03-22bibliografisk kontrollert
Inngår i avhandling
1. Brain Emotional Learning-Inspired Models
Åpne denne publikasjonen i ny fane eller vindu >>Brain Emotional Learning-Inspired Models
2014 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.

sted, utgiver, år, opplag, sider
Halmstad: Halmstad University Press, 2014. s. v, 31
Serie
Halmstad University Dissertations ; 8
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-25428 (URN)978-91-87045-16-5 (ISBN)978-91-87045-15-8 (ISBN)
Presentation
2014-06-17, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-06-02 Laget: 2014-05-27 Sist oppdatert: 2018-05-24bibliografisk kontrollert

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