TY - GEN
T1 - An ensemble of classifiers for coping with recurring contexts in data streams
AU - Katakis, Ioannis
AU - Tsoumakas, Grigorios
AU - Vlahavas, Ioannis
PY - 2008/6/1
Y1 - 2008/6/1
N2 - This paper proposes a general framework for classifying data streams by exploiting incremental clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual feature space is proposed. The clustering algorithm is then applied in order to group different concepts and identify recurring contexts. The ensemble is produced by maintaining an classifier for every concept discovered in the stream2.
AB - This paper proposes a general framework for classifying data streams by exploiting incremental clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual feature space is proposed. The clustering algorithm is then applied in order to group different concepts and identify recurring contexts. The ensemble is produced by maintaining an classifier for every concept discovered in the stream2.
UR - http://www.scopus.com/inward/record.url?scp=85051998172&partnerID=8YFLogxK
U2 - 10.3233/978-1-58603-891-5-763
DO - 10.3233/978-1-58603-891-5-763
M3 - Conference contribution
AN - SCOPUS:85051998172
SN - 978158603891
T3 - Frontiers in Artificial Intelligence and Applications
SP - 763
EP - 764
BT - Frontiers in Artificial Intelligence and Applications
PB - IOS Press
ER -