Name
Abstract | ||
|---|---|---|
In order to achieve distributed data mining quickly and efficiently, this paper proposes SMAJL, a model for sampling based multi-agent joint learning which integrates sampling technology and multi-agent argumentation in the field of association rule mining. By sampling, this model can reduce the size of dataset and improve the efficiency of data mining; through joint learning from argumentation, it can effectively integrate inconsistent knowledge from different samples to improve the quality of distributed mining. We experimentally show that, in a variety of sampling strategies, SMAJL can almost achieve 90% accuracy using sample having a size of only 30% of that of original dataset. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.5555/2615731.2617527 | AAMAS |
Keywords | Field | DocType |
multi-agent argumentation,inconsistent knowledge,multi-agent joint learning,sampling strategy,association rule mining,data mining,original dataset,different sample,joint learning | Data mining,Distributed mining,Computer science,Argumentation theory,Association rule learning,Artificial intelligence,Sampling (statistics),Machine learning | Conference |
Citations | PageRank | References |
2 | 0.36 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junyi Xu | 1 | 24 | 8.39 |
| Li Yao | 2 | 15 | 4.40 |
| Le Li | 3 | 2 | 1.72 |
| Yifan Chen | 4 | 474 | 70.39 |