Name
Abstract | ||
|---|---|---|
Data mining is used widely to mine hidden knowledge and information from huge data. Classification is an important task in data mining, and it has been successfully applied in various fields. We propose a multi-class classification method, Adaptive Distance-Based Voting Classification (ADVC), based on voting on the distances of the global training samples with adaptive and practical voting thresholds. Experiments on various datasets demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICMLC.2013.6890867 | ICMLC |
Keywords | Field | DocType |
voting,voting thresholds,one-class classification,pattern classification,multi-class classification,data mining,advc,adaptive distance-based voting classification | Pattern recognition,Voting,Computer science,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
04 | 2160-133X | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-Hua Hung | 1 | 0 | 0.68 |
| Shie-Jue Lee | 2 | 48 | 5.11 |