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Proceedings Paper

Research on classifying technique for imbalanced dataset based on Support Vector Machines
Author(s): Zhi-ming Yang; Yu Peng; Xi-yuan Peng
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Paper Abstract

It is shown that SVM can be ineffective in classifying the minority samples, when it is applied to the problem of learning from imbalanced datasets. To remedy this problem, this paper analyzes the true reason of negative effect to SVM classifier caused by data imbalance firstly. Based on this, a new method of shifting classifying hyperplane in the feature space is proposed, and its implementation method-Boundary Movement based on Sample Cutting Technique (BMSCT) is also described. Through theoretical analysis and empirical study, we show that our method augments the classification accuracy rate effectively without increasing the computation complexity.

Paper Details

Date Published: 12 January 2009
PDF: 6 pages
Proc. SPIE 7133, Fifth International Symposium on Instrumentation Science and Technology, 713320 (12 January 2009); doi: 10.1117/12.807706
Show Author Affiliations
Zhi-ming Yang, Harbin Institute of Technology (China)
Yu Peng, Harbin Institute of Technology (China)
Xi-yuan Peng, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 7133:
Fifth International Symposium on Instrumentation Science and Technology
Jiubin Tan; Xianfang Wen, Editor(s)

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