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		<title>Slawek: Created page with &quot;&lt;div style='display: none'&gt; == Do not edit this section == &lt;/div&gt; {{PublicationSetupTemplate|Author=Mahboobeh Parsapoor, Urban Bilstrup |PID=720689 |Name=Parsapoor, Mahboobeh ...&quot;</title>
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				<updated>2014-09-09T05:13:37Z</updated>
		
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|Name=Parsapoor, Mahboobeh [mahpar] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Centrum för forskning om inbyggda system (CERES) [6702]);Bilstrup, Urban [urban] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Centrum för forskning om inbyggda system (CERES) [6702])&lt;br /&gt;
|Title=An emotional learning-inspired ensemble classifier (ELiEC)&lt;br /&gt;
|PublicationType=Conference Paper&lt;br /&gt;
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|HostPublication=Proceedings of the 2013 Federated Conference on Computer Science and Information Systems (FedCSIS)&lt;br /&gt;
|Conference=2013 Federated Conference on Computer Science and Information Systems (FedCSIS), 8-11 September 2013, Krakow, Poland&lt;br /&gt;
|StartPage=137&lt;br /&gt;
|EndPage=141&lt;br /&gt;
|Year=2013&lt;br /&gt;
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|City=Los Alamitos, CA&lt;br /&gt;
|Publisher=IEEE Computer Society&lt;br /&gt;
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|ISBN=9781467344715&lt;br /&gt;
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|NBN=urn:nbn:se:hh:diva-25466&lt;br /&gt;
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|Keywords=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&lt;br /&gt;
|Categories=Datorsystem (20206)&lt;br /&gt;
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|Notes=&lt;br /&gt;
|Abstract=&amp;lt;p&amp;gt;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.&amp;lt;/p&amp;gt;&lt;br /&gt;
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|CreatedDate=2014-06-02&lt;br /&gt;
|PublicationDate=2014-06-02&lt;br /&gt;
|LastUpdated=2014-08-25&lt;br /&gt;
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