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Multi-Agent Based Multi-Knowledge Acquisition Method for Rough Set
Responsible organisation
2008 (English)Conference paper, (Refereed) Published
Abstract [en]

The key problem in knowledge acquisition algorithm is how to deal with large-scale datasets and extract small number of compact rules. In recent years, several approaches to distributed data mining have been developed, but only a few of them benefit rough set based knowledge acquisition methods. This paper is intended to combine multi-agent technology into rough set based knowledge acquisition method. We briefly review the multi-knowledge acquisition algorithm, and propose a novel approach of distributed multi-knowledge acquisition method. Information system is decomposed into sub-systems by independent partition attribute set. Agent based knowledge acquisition tasks depend on universes of sub-systems, and the agent-oriented implementation is discussed. The main advantage of the method is that it is efficient on large-scale datasets and avoids generating excessive rules. Finally, the capabilities of our method are demonstrated on several datasets and results show that rules acquired are compact, having classification accuracy comparable to state-of-the-art methods.

Place, publisher, year, edition, pages
Chendu: Springer , 2008.
Keyword [en]
attribute reduction, multi-agent technology, knowledge acquisition, classification accuracy
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-8407DOI: 10.1007/978-3-540-79721-0_23Local ID: oai:bth.se:forskinfo040F47905EF4A2BCC12574AC00406B8CISBN: 978-3-540-79720-3 (print)OAI: oai:DiVA.org:bth-8407DiVA: diva2:836130
Conference
Rough Sets and Knowledge Technology, Third International Conference, RSKT 2008
Available from: 2012-09-18 Created: 2008-08-21 Last updated: 2015-06-30Bibliographically approved

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Bai, Guohua
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
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  • asciidoc
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