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ECML
2004
Springer

Applying Support Vector Machines to Imbalanced Datasets

13 years 9 months ago
Applying Support Vector Machines to Imbalanced Datasets
Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud). This paper discusses the factors behind this failure and explains why the common strategy of undersampling the training data may not be the best choice for SVM. We then propose an algorithm for overcoming these problems which is based on a variant of the SMOTE algorithm by Chawla et al, combined with Veropoulos et al’s different error costs algorithm. We compare the performance of our algorithm against these two algorithms, along with undersampling and regular SVM and show that our algorithm outperforms all of them.
Rehan Akbani, Stephen Kwek, Nathalie Japkowicz
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ECML
Authors Rehan Akbani, Stephen Kwek, Nathalie Japkowicz
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