An Empirical Study on the Performance of Cost-Sensitive Boosting Algorithms with Different Levels of Class Imbalance
Joint Authors
Zhang, Chun-Xia
Yin, Qing-Yan
Liu, Sheng-Cai
Zhang, Jiang-She
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-09-19
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Cost-sensitive boosting algorithms have proven successful for solving the difficult class imbalance problems.
However, the influence of misclassification costs and imbalance level on the algorithm performance is still not clear.
The present paper aims to conduct an empirical comparison of six representative cost-sensitive boosting algorithms, including AdaCost, CSB1, CSB2, AdaC1, AdaC2, and AdaC3.
These algorithms are thoroughly evaluated by a comprehensive suite of experiments, in which nearly fifty thousands classification models are trained on 17 real-world imbalanced data sets.
Experimental results show that AdaC serial algorithms generally outperform AdaCost and CSB when dealing with different imbalance level data sets.
Furthermore, the optimality of AdaC2 algorithm stands out around the misclassification costs setting: CN=0.7, CP=1, especially for dealing with strongly imbalanced data sets.
In the case of data sets with a low-level imbalance, there is no significant difference between the AdaC serial algorithms.
In addition, the results indicate that AdaC1 is comparatively insensitive to the misclassification costs, which is consistent with the finding of the preceding research work.
American Psychological Association (APA)
Yin, Qing-Yan& Zhang, Jiang-She& Zhang, Chun-Xia& Liu, Sheng-Cai. 2013. An Empirical Study on the Performance of Cost-Sensitive Boosting Algorithms with Different Levels of Class Imbalance. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-1032147
Modern Language Association (MLA)
Yin, Qing-Yan…[et al.]. An Empirical Study on the Performance of Cost-Sensitive Boosting Algorithms with Different Levels of Class Imbalance. Mathematical Problems in Engineering No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-1032147
American Medical Association (AMA)
Yin, Qing-Yan& Zhang, Jiang-She& Zhang, Chun-Xia& Liu, Sheng-Cai. An Empirical Study on the Performance of Cost-Sensitive Boosting Algorithms with Different Levels of Class Imbalance. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-1032147
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032147