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New construction of ensemble classifiers for imbalanced datasets

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Abstract
To improve the predictive power of classifiers against imbalanced data sets, this paper presents an ensemble-based learning algorithm as a new ensemble classifier model called as an SVM-C5.0 ensemble classifier model, SCECM. The SCECM adopts a differentiated sampling rate algorithm based on an improved Adaboost algorithm and further employs some unique classifier-selection strategy, novel classifier integration approach and original classification decision-making method. Comparative experimental results show that the proposed approach improves performance for the minority class while preserving the ability to recognize examples from the majority classes.
Keywords
classification, SMOTE, ensemble model of classifiers, Data mining, imbalanced datasets, differentiated sampling rate, heterogeneous classifier

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Citation

Please use this url to cite or link to this publication:

Chicago
Zhai, Yun, Bingru Yang, Nan Ma, and Da Ruan. 2012. “New Construction of Ensemble Classifiers for Imbalanced Datasets.” Journal of Multiple-valued Logic and Soft Computing 18 (5-6): 599–616.
APA
Zhai, Y., Yang, B., Ma, N., & Ruan, D. (2012). New construction of ensemble classifiers for imbalanced datasets. JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, 18(5-6), 599–616.
Vancouver
1.
Zhai Y, Yang B, Ma N, Ruan D. New construction of ensemble classifiers for imbalanced datasets. JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING. 2012;18(5-6):599–616.
MLA
Zhai, Yun, Bingru Yang, Nan Ma, et al. “New Construction of Ensemble Classifiers for Imbalanced Datasets.” JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 18.5-6 (2012): 599–616. Print.
@article{5835938,
  abstract     = {To improve the predictive power of classifiers against imbalanced data sets, this paper presents an ensemble-based learning algorithm as a new ensemble classifier model called as an SVM-C5.0 ensemble classifier model, SCECM. The SCECM adopts a differentiated sampling rate algorithm based on an improved Adaboost algorithm and further employs some unique classifier-selection strategy, novel classifier integration approach and original classification decision-making method. Comparative experimental results show that the proposed approach improves performance for the minority class while preserving the ability to recognize examples from the majority classes.},
  author       = {Zhai, Yun and Yang, Bingru and Ma, Nan and Ruan, Da},
  issn         = {1542-3980},
  journal      = {JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING},
  keywords     = {classification,SMOTE,ensemble model of classifiers,Data mining,imbalanced datasets,differentiated sampling rate,heterogeneous classifier},
  language     = {eng},
  number       = {5-6},
  pages        = {599--616},
  title        = {New construction of ensemble classifiers for imbalanced datasets},
  volume       = {18},
  year         = {2012},
}

Web of Science
Times cited: