A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling
Joint Authors
Ji, Nan-Nan
Zhang, Chun-Xia
Zhang, Jiang-She
Yin, Qing-Yan
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-30
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Learning with imbalanced data is one of the emergent challenging tasks in machine learning.
Recently, ensemble learning has arisen as an effective solution to class imbalance problems.
The combination of bagging and boosting with data preprocessing resampling, namely, the simplest and accurate exploratory undersampling, has become the most popular method for imbalanced data classification.
In this paper, we propose a novel selective ensemble construction method based on exploratory undersampling, RotEasy, with the advantage of improving storage requirement and computational efficiency by ensemble pruning technology.
Our methodology aims to enhance the diversity between individual classifiers through feature extraction and diversity regularized ensemble pruning.
We made a comprehensive comparison between our method and some state-of-the-art imbalanced learning methods.
Experimental results on 20 real-world imbalanced data sets show that RotEasy possesses a significant increase in performance, contrasted by a nonparametric statistical test and various evaluation criteria.
American Psychological Association (APA)
Yin, Qing-Yan& Zhang, Jiang-She& Zhang, Chun-Xia& Ji, Nan-Nan. 2014. A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-465658
Modern Language Association (MLA)
Yin, Qing-Yan…[et al.]. A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling. Mathematical Problems in Engineering No. 2014 (2014), pp.1-14.
https://search.emarefa.net/detail/BIM-465658
American Medical Association (AMA)
Yin, Qing-Yan& Zhang, Jiang-She& Zhang, Chun-Xia& Ji, Nan-Nan. A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-465658
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-465658