Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift
Joint Authors
Ortíz Díaz, Agustín
del Campo-Ávila, José
Ramos-Jiménez, Gonzalo
Frías Blanco, Isvani
Caballero Mota, Yailé
Mustelier Hechavarría, Antonio
Morales-Bueno, Rafael
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-23
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear.
This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts.
FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism.
FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear.
We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets.
The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts.
American Psychological Association (APA)
Ortíz Díaz, Agustín& del Campo-Ávila, José& Ramos-Jiménez, Gonzalo& Frías Blanco, Isvani& Caballero Mota, Yailé& Mustelier Hechavarría, Antonio…[et al.]. 2015. Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1078582
Modern Language Association (MLA)
Ortíz Díaz, Agustín…[et al.]. Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift. The Scientific World Journal No. 2015 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1078582
American Medical Association (AMA)
Ortíz Díaz, Agustín& del Campo-Ávila, José& Ramos-Jiménez, Gonzalo& Frías Blanco, Isvani& Caballero Mota, Yailé& Mustelier Hechavarría, Antonio…[et al.]. Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1078582
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078582