A Case Study of SVM Extension Techniques on classification of Imbalanced Data

Lee, K. K., Harris, C. J., Gunn, S. R. and Reed, P. A. S. (2001) A Case Study of SVM Extension Techniques on classification of Imbalanced Data. In: Congress on Neural Networks and Applications, Fuzzy Sets and Fuzzy Systems and Evolutionary Computing, 2001-02-11 - 2001-02-15.

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Abstract

In many classification problems the data is imbalanced, that is the class priors are different. Here, we consider the classification problem of fatigue crack initiation in automotive camshafts, where this imbalance is significant. The extension techniques of Support Vector Machine (SVM) - the Control Sensitivity (CSSVM) and Adaptive Margin (AMSVM) - which offer different ways of dealing with imbalanced data was investigated. Geometric mean was used to evaluate the performance of the model. The CSSVM has outperformed the AMSVM. The use of different kernels did not produce significant changes in the results. The ratio between the misclassification cost and the training size for each class is very similar, indicating a strong relationship between them.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences
Depositing User:	Vishal Gautam
Date Deposited:	25 Aug 2011 12:31
Last Modified:	15 Dec 2022 01:06
URI:	https://ueaeprints.uea.ac.uk/id/eprint/21755
DOI:

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