Name
Abstract | ||
|---|---|---|
Rare category detecion RCD aims to discover rare categories in a massive unlabeled data set with the help of a labeling oracle. A challenging task in RCD is to discover rare categories which are concealed by numerous data examples from major categories. Only a few algorithms have been proposed for this issue, most of which are on quadratic or cubic time complexity. In this paper, we propose a novel tree-based algorithm known as RCD-Forest with $$O\\varphi n \\log {n/s}$$ time complexity and high query efficiency where n is the size of the unlabeled data set. Experimental results on both synthetic and real data sets verify the effectiveness and efficiency of our method. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25159-2_55 | KSEM |
Keywords | Field | DocType |
Rare category detection,Relative density,Compact tree | Data mining,Data set,Computer science,Oracle,Quadratic equation,Artificial intelligence,Time complexity,Machine learning | Conference |
Volume | ISSN | Citations |
9403 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiqin Weng | 1 | 3 | 0.75 |
| Zhenguang Liu | 2 | 47 | 5.09 |
| Kevin Chiew | 3 | 116 | 11.06 |
| Qinming He | 4 | 371 | 41.53 |