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Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 5036710, 13 pages
Research Article

Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

Department of Computer Science, Taiyuan Normal University, Taiyuan 030012, China

Correspondence should be addressed to Jianhong Yan

Received 3 June 2017; Revised 1 October 2017; Accepted 6 November 2017; Published 23 January 2018

Academic Editor: Michele Migliore

Copyright © 2018 Jianhong Yan and Suqing Han. 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.


Learning with imbalanced data sets is considered as one of the key topics in machine learning community. Stacking ensemble is an efficient algorithm for normal balance data sets. However, stacking ensemble was seldom applied in imbalance data. In this paper, we proposed a novel RE-sample and Cost-Sensitive Stacked Generalization (RECSG) method based on 2-layer learning models. The first step is Level 0 model generalization including data preprocessing and base model training. The second step is Level 1 model generalization involving cost-sensitive classifier and logistic regression algorithm. In the learning phase, preprocessing techniques can be embedded in imbalance data learning methods. In the cost-sensitive algorithm, cost matrix is combined with both data characters and algorithms. In the RECSG method, ensemble algorithm is combined with imbalance data techniques. According to the experiment results obtained with 17 public imbalanced data sets, as indicated by various evaluation metrics (AUC, GeoMean, and AGeoMean), the proposed method showed the better classification performances than other ensemble and single algorithms. The proposed method is especially more efficient when the performance of base classifier is low. All these demonstrated that the proposed method could be applied in the class imbalance problem.