![](/images/graphics-bg.png)
An Improved Oversampling Algorithm Based on the Samples’ Selection Strategy for Classifying Imbalanced Data
Joint Authors
Xie, Wenhao
Liang, Gongqian
Dong, Zhonghui
Tan, Baoyu
Zhang, Baosheng
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-06
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The imbalance data refers to at least one of its classes which is usually outnumbered by the other classes.
The imbalanced data sets exist widely in the real world, and the classification for them has become one of the hottest issues in the field of data mining.
At present, the classification solutions for imbalanced data sets are mainly based on the algorithm-level and the data-level.
On the data-level, both oversampling strategies and undersampling strategies are used to realize the data balance via data reconstruction.
SMOTE and Random-SMOTE are two classic oversampling algorithms, but they still possess the drawbacks such as blind interpolation and fuzzy class boundaries.
In this paper, an improved oversampling algorithm based on the samples’ selection strategy for the imbalanced data classification is proposed.
On the basis of the Random-SMOTE algorithm, the support vectors (SV) are extracted and are treated as the parent samples to synthesize the new examples for the minority class in order to realize the balance of the data.
Lastly, the imbalanced data sets are classified with the SVM classification algorithm.
F-measure value, G-mean value, ROC curve, and AUC value are selected as the performance evaluation indexes.
Experimental results show that this improved algorithm demonstrates a good classification performance for the imbalanced data sets.
American Psychological Association (APA)
Xie, Wenhao& Liang, Gongqian& Dong, Zhonghui& Tan, Baoyu& Zhang, Baosheng. 2019. An Improved Oversampling Algorithm Based on the Samples’ Selection Strategy for Classifying Imbalanced Data. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1195184
Modern Language Association (MLA)
Xie, Wenhao…[et al.]. An Improved Oversampling Algorithm Based on the Samples’ Selection Strategy for Classifying Imbalanced Data. Mathematical Problems in Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1195184
American Medical Association (AMA)
Xie, Wenhao& Liang, Gongqian& Dong, Zhonghui& Tan, Baoyu& Zhang, Baosheng. An Improved Oversampling Algorithm Based on the Samples’ Selection Strategy for Classifying Imbalanced Data. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1195184
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195184