An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams
| dc.contributor.author | Kranen, Philipp | |
| dc.contributor.author | Assent, Ira | |
| dc.contributor.author | Seidl, Thomas | |
| dc.date.accessioned | 2018-01-10T13:18:24Z | |
| dc.date.available | 2018-01-10T13:18:24Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | Due to the ever growing presence of data streams there has been a considerable amount of research on stream data mining over the past years. Anytime algorithms are particularly well suited for stream mining, since they flexibly use all available time on streams of varying data rates, and are also shown to outperform traditional budget approaches on constant streams. In this article we present an index-inspired algorithm for Bayesian anytime classification on evolving data streams and show its performance on benchmark data sets. | |
| dc.identifier.pissn | 1610-1995 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11641 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Datenbank-Spektrum: Vol. 12, No. 1 | |
| dc.relation.ispartofseries | Datenbank-Spektrum | |
| dc.subject | Anytime algorithms | |
| dc.subject | Data mining | |
| dc.subject | Stream processing | |
| dc.title | An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 50 | |
| gi.citation.startPage | 43 |