Towards Cooperative Predictive Data Mining in Competitive Environments.
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00157250" target="_blank" >RIV/68407700:21230/09:00157250 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Towards Cooperative Predictive Data Mining in Competitive Environments.
Popis výsledku v původním jazyce
We study the problem of predictive data mining in a competitive multi-agent setting, in which each agent is assumed to have some partial knowledge required for correctly classifying a set of unlabelled examples. The agents are self-interested and therefore need to reason about the trade-offs between increasing their classification accuracy by collaborating with other agents and disclosing their private classification knowledge to other agents through such collaboration. We analyze the problem and propose a set of components which can enable cooperation in this otherwise competitive task. These components include measures for quantifying private knowledge disclosure, data-mining models suitable for multi-agent predictive data mining, and a set of strategies by which agents can improve their classification accuracy through collaboration. The overall framework and its individual components are validated on a synthetic experimental domain.
Název v anglickém jazyce
Towards Cooperative Predictive Data Mining in Competitive Environments.
Popis výsledku anglicky
We study the problem of predictive data mining in a competitive multi-agent setting, in which each agent is assumed to have some partial knowledge required for correctly classifying a set of unlabelled examples. The agents are self-interested and therefore need to reason about the trade-offs between increasing their classification accuracy by collaborating with other agents and disclosing their private classification knowledge to other agents through such collaboration. We analyze the problem and propose a set of components which can enable cooperation in this otherwise competitive task. These components include measures for quantifying private knowledge disclosure, data-mining models suitable for multi-agent predictive data mining, and a set of strategies by which agents can improve their classification accuracy through collaboration. The overall framework and its individual components are validated on a synthetic experimental domain.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
JC - Počítačový hardware a software
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
Z - Vyzkumny zamer (s odkazem do CEZ)
Ostatní
Rok uplatnění
2009
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Agents and Data Mining Interaction
ISBN
978-3-642-03602-6
ISSN
0302-9743
e-ISSN
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Počet stran výsledku
14
Strana od-do
95-108
Název nakladatele
Springer
Místo vydání
Heidelberg
Místo konání akce
Budapest
Datum konání akce
10. 5. 2009
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000269949700008