An Index-inspired Algorithm for Anytime Classification on Evolving Data Streams
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.
|Authors:||Kranen P., Assent I., Seidl T.|
|Published in:||Datenbank-Spektrum (Springer DASP), Volume 12, Issue 1|
|Url:||DASP Journal - full text|
|Type:||Journal Articles (peer reviewed)|
|Research topic:||Data Analysis and Knowledge Extraction|