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Abstract
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Article Information:
Imbalanced Classification Based on Active Learning SMOTE
Ying Mi
Corresponding Author: Ying Mi
Submitted: June 20, 2012
Accepted: July 23, 2012
Published: January 21, 2013 |
Abstract:
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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.
Key words: Active learning, imbalanced data set, SMOTE, support vector machine, , ,
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Cite this Reference:
Ying Mi, . Imbalanced Classification Based on Active Learning SMOTE. Research Journal of Applied Sciences, Engineering and Technology, (03): 944-949.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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