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Dynamic ensemble selection for multi-class imbalanced datasets.
- Source :
-
Information Sciences . Jun2018, Vol. 445, p22-37. 16p. - Publication Year :
- 2018
-
Abstract
- Many real-world classification tasks suffer from the class imbalanced problem, in which some classes are highly underrepresented as compared to other classes. In this paper, we focus on multi-class imbalance problems which are considerably more difficult to address than two-class imbalanced problems. On this account, we develop a novel and effective procedure, called dynamic ensemble selection for multi-class imbalanced datasets (DES-MI), in which the competence of the candidate classifiers are assessed with weighted instances in the neighborhood. The proposed DES-MI consists of two key components: the generation of balanced training datasets and the selection of appropriate classifiers. To do so, we develop a preprocessing procedure to balance the training dataset which relies on random balance. To select the most appropriate classifiers in the scenario of multi-class imbalance problems, we propose a weighting mechanism to highlight the competence of classifiers that are more powerful in classifying examples in the region of underrepresented competence. We develop a thorough experimental study in order to verify the benefits of DES-MI in handling multi-class imbalanced datasets. The obtained results, supported by the proper statistical analysis, indicate that DES-MI is able to improve the classification performance for multi-class imbalanced datasets. [ABSTRACT FROM AUTHOR]
- Subjects :
- *BIG data
*DATA mining
*DATA distribution
*SUPERVISED learning
*DATA analysis
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 445
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 128611683
- Full Text :
- https://doi.org/10.1016/j.ins.2018.03.002