GEP-Based ensemble classifier with drift-detection
Joanna Jędrzejowicz , Piotr Jędrzejowicz
AbstractThe paper proposes a new ensemble classifier using Gene Expression Programming as the induction engine. The approach aims at predicting unknown class labels for datasets with concept drift. For constructing the proposed ensemble we use the two-level scheme where at the lower level base classifiers are induced and at the upper level, the meta-classifier is produced. The classification process is controlled by the well-known early drift detection mechanism. To validate the approach computational experiment has been carried out. Its results confirmed that the proposed classifier performs well.
|Publication size in sheets||0.5|
|Book||Bramer Max, Petridis Miltos (eds.): Artificial Intelligence XXXV: 38th SGAI International Conference on Artificial Intelligence, AI 2018 Cambridge, UK, December 11-13, 2018: proceedings, Lecture Notes In Computer Science, no. 11311, 2018, Springer, ISBN 978-3-030-04190-8, [978-3-030-04191-5], 468 p., DOI:10.1007/978-3-030-04191-5_9|
|Keywords in English||gene expression programming, classifier ensemble, datasets with concept drif|
|Score||= 5.0, 29-11-2018, BookChapterNotSeriesMainLanguages|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.