Adaptive mechanisms for classification problems with drifting data
Authors: Sahel, Z., Bouchachia, A., Gabrys, B. and Rogers, P.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 4693 LNAI
Issue: PART 2
Pages: 419-426
eISSN: 1611-3349
ISBN: 9783540748267
ISSN: 0302-9743
DOI: 10.1007/978-3-540-74827-4_53
Abstract:Most work on supervised learning is undertaken on static problems. However, in many real world classification problems, the environment in which the classifiers operate is dynamic i.e. the descriptions of classes change with time. In this paper, the process of generating drifting data is introduced in order to assess two adaptive approaches that deal with dynamically changing data. These approaches are: retraining on evolving data set and incremental learning. The empirical evaluation has shown that both these approaches improve the performance compared to the non-adaptive mode though a number of outstanding research issues remain. © Springer-Verlag Berlin Heidelberg 2007.
Source: Scopus
Adaptive mechanisms for classification problems with drifting data
Authors: Sahel, Z., Bouchachia, A., Gabrys, B. and Rogers, P.
Journal: KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS: KES 2007 - WIRN 2007, PT II, PROCEEDINGS
Volume: 4693
Pages: 419-426
eISSN: 1611-3349
ISBN: 978-3-540-74826-7
ISSN: 0302-9743
Source: Web of Science (Lite)
Adaptive Mechanisms for Classification Problems with Drifting Data
Authors: Sahel, Z., Bouchachia, A., Gabrys, B. and Rogers, P.
Editors: Apolloni, A., Howlett, R.J. and Jain, L.
Pages: 419-426
Publisher: Springer
Place of Publication: Berlin
ISBN: 978-3-540-74826-7
DOI: 10.1007/978-3-540-74827-4_53
Abstract:Most work on supervised learning is undertaken on static problems. However, in many real world classification problems, the environment in which the classifiers operate is dynamic i.e. the descriptions of classes change with time. In this paper, the process of generating drifting data is introduced in order to assess two adaptive approaches that deal with dynamically changing data. These approaches are: retraining on evolving data set and incremental learning. The empirical evaluation has shown that both these approaches improve the performance compared to the non-adaptive mode though a number of outstanding research issues remain.
http://www.springerlink.com/content/n0286t8411252664/?p=dabdbe745320484d9b56fb79525cb888&pi=17
Source: Manual
Preferred by: Hamid Bouchachia
Adaptive Mechanisms for Classification Problems with Drifting Data.
Authors: Sahel, Z., Bouchachia, A., Gabrys, B. and Rogers, P.
Editors: Apolloni, B., Howlett, R.J. and Jain, L.C.
Journal: KES (2)
Volume: 4693
Pages: 419-426
Publisher: Springer
ISBN: 978-3-540-74826-7
https://doi.org/10.1007/978-3-540-74827-4
Source: DBLP