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Mathematical Problems in Engineering
Volume 2013, Article ID 761814, 12 pages
Research Article

An Empirical Study on the Performance of Cost-Sensitive Boosting Algorithms with Different Levels of Class Imbalance

School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China

Received 10 May 2013; Accepted 13 August 2013

Academic Editor: Reza Jazar

Copyright © 2013 Qing-Yan Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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: , , 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.