On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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http://dx.doi.org/10.1016/j.knosys.2011.06.013 |
Metadata
Title
On the effectiveness of preprocessing methods when dealing with different levels of class imbalanceDate
2012Publisher
ElsevierISSN
0950-7051Bibliographic citation
Knowledge-Based Systems (Feb. 2012) vol. 25, no. 1, p. 13-21Type
info:eu-repo/semantics/articlePublisher version
http://www.sciencedirect.com/science/article/pii/S0950705111001286Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how ... [+]
The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how learning is affected when different resampling algorithms transform the originally imbalanced data into artificially balanced class distributions. Experiments over 17 real data sets using eight different classifiers, four resampling algorithms and four performance evaluation measures show that over-sampling the minority class consistently outperforms under-sampling the majority class when data sets are strongly imbalanced, whereas there are not significant differences for databases with a low imbalance. Results also indicate that the classifier has a very poor influence on the effectiveness of the resampling strategies [-]
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© 2012 Elsevier Inc. All rights reserved
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