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Multi-agent based classification using argumentation from experience.

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Title: Multi-agent based classification using argumentation from experience.
Authors: Wardeh, Maya1 maya.wardeh@liverpool.ac.uk, Coenen, Frans1 coenen@liverpool.ac.uk, Bench-Capon, Trevor1 tbc@liverpool.ac.uk
Source: Autonomous Agents & Multi-Agent Systems. Nov2012, Vol. 25 Issue 3, p447-474. 28p.
Subjects: Multiagent systems, Data mining, Data integration, Database management, Machine learning, Self-organizing systems
Abstract: An approach to classification using a multi-agent system founded on an Argumentation from Experience paradigm is proposed. The technique is based on the idea that classification can be conducted as a process whereby a group of agents 'argue' about the classification of a given case according to their experience as recorded in individual local data sets. The paper describes mechanisms whereby this can be achieved, which have been realised in the PISA framework. The framework allows both the possibility of agents operating in groups (coalitions) and migrating between groups. The proposed multi-agent classification using the Argumentation from Experience paradigm has been used to address standard, ordinal and unbalanced classification problems with good results. A full evaluation, in the context of these applications, is presented. [ABSTRACT FROM AUTHOR]
Copyright of Autonomous Agents & Multi-Agent Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Autonomous+Agents+%26+Multi-Agent+Systems%22">Autonomous Agents & Multi-Agent Systems</searchLink>. Nov2012, Vol. 25 Issue 3, p447-474. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink><br /><searchLink fieldCode="DE" term="%22Database+management%22">Database management</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Self-organizing+systems%22">Self-organizing systems</searchLink>
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  Data: An approach to classification using a multi-agent system founded on an Argumentation from Experience paradigm is proposed. The technique is based on the idea that classification can be conducted as a process whereby a group of agents 'argue' about the classification of a given case according to their experience as recorded in individual local data sets. The paper describes mechanisms whereby this can be achieved, which have been realised in the PISA framework. The framework allows both the possibility of agents operating in groups (coalitions) and migrating between groups. The proposed multi-agent classification using the Argumentation from Experience paradigm has been used to address standard, ordinal and unbalanced classification problems with good results. A full evaluation, in the context of these applications, is presented. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Autonomous Agents & Multi-Agent Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Nov2012
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