Name
Title | ||
|---|---|---|
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining |
Abstract | ||
|---|---|---|
For many real world problems we must perform classification under widely varying amounts of computational resources. For example, if asked to classify an instance taken from a bursty stream, we may have from milliseconds to minutes to return a class prediction. For such problems an anytime algorithm may be especially useful. In this work we show how we can convert the ubiquitous nearest neighbor classifier into an anytime algorithm that can produce an instant classification, or if given the luxury of additional time, can utilize the extra time to increase classification accuracy. We demonstrate the utility of our approach with a comprehensive set of experiments on data from diverse domains. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICDM.2006.21 | ICDM |
Keywords | Field | DocType |
data mining,pattern classification,anytime classification,bursty stream,computational resources,nearest neighbor algorithm,stream mining,ubiquitous nearest neighbor classifier | Data mining,Ball tree,Best bin first,Computer science,Artificial intelligence,Nearest-neighbor chain algorithm,Anytime algorithm,Large margin nearest neighbor,k-nearest neighbors algorithm,Pattern recognition,Cover tree,Machine learning,Nearest neighbor classifier | Conference |
ISSN | ISBN | Citations |
1550-4786 | 0-7695-2701-9 | 58 |
PageRank | References | Authors |
2.11 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ken Ueno | 1 | 124 | 13.27 |
| Xiaopeng Xi | 2 | 531 | 24.76 |
| Eamonn J. Keogh | 3 | 11859 | 645.93 |
| Dah-Jye Lee | 4 | 422 | 42.05 |