21 search results - page 2 / 5 » Categorizing Concepts for Detecting Drifts in Stream |
KDD
1998
ACM
13 years 9 months ago
1998
ACM
The task in the computer security domain of anomaly detection is to characterize the behaviors of a computer user (the `valid', or `normal' user) so that unusual occurre...
ICTAI
2007
IEEE
13 years 11 months ago
2007
IEEE
An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classifiers by using Genetic Programmi...
ASC
2011
12 years 12 months ago
2011
In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact tha...
ICDM
2008
IEEE
13 years 11 months ago
2008
IEEE
To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas a reactive learner predicts ba...
ASC
2008
13 years 5 months ago
2008
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...