TY - GEN
T1 - Efficient learning algorithms for agents mining time-changing data streams
AU - Cohen, Lior
AU - Avrahami, Gil
AU - Last, Mark
AU - Kandel, Abraham
PY - 2006/1/1
Y1 - 2006/1/1
N2 - Many continuously recorded data streams are generated by non-stationary processes, which may change over time, in some cases even drastically. Some adaptive learning agents deal with time-changing data streams by generating a new model from every incoming window of training examples. Though this solution should ensure an accurate and relevant model at all times, it may waste significant computational resources on continuous re-generation of nearly identical models during periods of stability. In this paper, we evaluate a series of efficient incremental algorithms that are nearly as accurate as existing online methods, sometimes even outperforming them, while being considerably cheaper in terms of the processing time. The proposed incremental techniques are based on the Information Network classification algorithm. The incremental methods efficiency is demonstrated on realworld streams of road traffic and intrusion detection data.
AB - Many continuously recorded data streams are generated by non-stationary processes, which may change over time, in some cases even drastically. Some adaptive learning agents deal with time-changing data streams by generating a new model from every incoming window of training examples. Though this solution should ensure an accurate and relevant model at all times, it may waste significant computational resources on continuous re-generation of nearly identical models during periods of stability. In this paper, we evaluate a series of efficient incremental algorithms that are nearly as accurate as existing online methods, sometimes even outperforming them, while being considerably cheaper in terms of the processing time. The proposed incremental techniques are based on the Information Network classification algorithm. The incremental methods efficiency is demonstrated on realworld streams of road traffic and intrusion detection data.
UR - http://www.scopus.com/inward/record.url?scp=38849187287&partnerID=8YFLogxK
U2 - 10.1109/CIMCA.2006.92
DO - 10.1109/CIMCA.2006.92
M3 - Conference contribution
AN - SCOPUS:38849187287
SN - 0769527310
SN - 9780769527314
T3 - CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ...
BT - CIMCA 2006
PB - Institute of Electrical and Electronics Engineers
T2 - CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies and International Commerce
Y2 - 28 November 2006 through 1 December 2006
ER -