Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03603-3_8 | ADMI |
Keywords | Field | DocType |
individual component,towards cooperative predictive data,predictive data mining,partial knowledge,competitive environments,classification accuracy,multi-agent predictive data mining,private classification knowledge,data-mining model,competitive task,competitive multi-agent setting,private knowledge disclosure,data mining | Data mining,Feature vector,Computer science,Association rule learning,Mutual information,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
5680 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viliam Lisý | 1 | 219 | 26.66 |
| Michal Jakob | 2 | 204 | 24.81 |
| Petr Benda | 3 | 25 | 4.03 |
| Štěpán Urban | 4 | 16 | 2.30 |
| Michal Pěchouček | 5 | 1134 | 133.88 |