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Adaptive neighbor synthetic minority oversampling technique under 1NN outcast handling.
- Source :
-
Songklanakarin Journal of Science & Technology . Sep/Oct2017, Vol. 39 Issue 5, p565-576. 12p. - Publication Year :
- 2017
-
Abstract
- SMOTE is an effective oversampling technique for a class imbalance problem due to its simplicity and relatively high recall value. One drawback of SMOTE is a requirement of the number of nearest neighbors as a key parameter to synthesize instances. This paper introduces a new adaptive algorithm called Adaptive neighbor Synthetic Minority Oversampling Technique (ANS) to dynamically adapt the number of neighbors needed for oversampling around different minority regions. This technique also defines a minority outcast as a minority instance having no minority class neighbors. Minority outcasts are neglected by most oversampling techniques but instead, an additional outcast handling method is proposed for the performance improvement via a 1-nearest neighbor model. Based on our experiments in UCI and PROMISE datasets, generated datasets from this technique have improved the accuracy performance of a classification, and the improvement can be verified statistically by the Wilcoxon signed-rank test. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01253395
- Volume :
- 39
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Songklanakarin Journal of Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 126124749