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Noise Reduction A Priori Synthetic Over-Sampling for class imbalanced data sets.
- Source :
-
Information Sciences . Oct2017, Vol. 408, p146-161. 16p. - Publication Year :
- 2017
-
Abstract
- In real world data set the underlying data distribution may be highly skewed. Building accurate classifiers for predicting group membership is made difficult because the classifier has a tendency to be biased towards the over represented or majority group as a result. This problem is referred to as a class imbalance problem. Re-sampling techniques that produce new samples by means of over-sampling aim to combat class imbalance by increasing the number of members that belong to the minority group. This paper introduces a new over-sampling technique that focuses on noise reduction and selective sampling of the minority group which results in improvement for prediction of minority group membership. Experiments are conducted across a wide range of data sets, learners and over sampling methods. The results for this new method show improvement for Sensitivity and Gmean measures over the compared approaches. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 408
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 123196017
- Full Text :
- https://doi.org/10.1016/j.ins.2017.04.046