A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-10
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Textual stream classification has become a realistic and challenging issue since large-scale, high-dimensional, and non-stationary streams with class imbalance have been widely used in various real-life applications.
According to the characters of textual streams, it is technically difficult to deal with the classification of textual stream, especially in imbalanced environment.
In this paper, we propose a new ensemble framework, clustering forest, for learning from the textual imbalanced stream with concept drift (CFIM).
The CFIM is based on ensemble learning by integrating a set of clustering trees (CTs).
An adaptive selection method, which flexibly chooses the useful CTs by the property of the stream, is presented in CFIM.
In particular, to deal with the problem of class imbalance, we collect and reuse both rare-class instances and misclassified instances from the historical chunks.
Compared to most existing approaches, it is worth pointing out that our approach assumes that both majority class and rareclass may suffer from concept drift.
Thus the distribution of resampled instances is similar to the current concept.
The effectiveness of CFIM is examined in five real-world textual streams under an imbalanced nonstationary environment.
Experimental results demonstrate that CFIM achieves better performance than four state-of-the-art ensemble models.
American Psychological Association (APA)
Song, Ge& Ye, Yunming. 2014. A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1049848
Modern Language Association (MLA)
Song, Ge& Ye, Yunming. A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1049848
American Medical Association (AMA)
Song, Ge& Ye, Yunming. A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1049848
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049848