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2013 (Vol. 5, Issue: 03)
Article Information:

Imbalanced Classification Based on Active Learning SMOTE

Ying Mi
Corresponding Author:  Ying Mi 

Key words:  Active learning, imbalanced data set, SMOTE, support vector machine, , ,
Vol. 5 , (03): 944-949
Submitted Accepted Published
June 20, 2012 July 23, 2012 January 21, 2013
Abstract:

In real-world problems, the data sets are typically imbalanced. Imbalance has a serious impact on the performance of classifiers. SMOTE is a typical over-sampling technique which can effectively balance the imbalanced data. However, it brings noise and other problems affecting the classification accuracy. To solve this problem, this study introduces the classification performance of support vector machine and presents an approach based on active learning SMOTE to classify the imbalanced data. Experimental results show that the proposed method has higher Area under the ROC Curve, F-measure and G-mean values than many existing class imbalance learning methods.
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  Cite this Reference:
Ying Mi, 2013. Imbalanced Classification Based on Active Learning SMOTE.  Research Journal of Applied Sciences, Engineering and Technology, 5(03): 944-949.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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