> In SVM Classification
> if the training data are unbalanced,
> e.g., lots of classA (10000) but few classB (100)
> how to justify the data to enhance the SVC model
What I do is to select a different error weighting for each class,
eg, C+=1, C-=100, to even out the biasing caused by the dataset.
You might want to have a look at
H.G. Chew, R.E. Bogner, and C.C. Lim. Target detection in radar imagery
using support vector machines with training size biasing. In International
Conference on Control, Automation, Robotics and Vision, ICARCV 2000,
Singapore, pages CD-ROM, 2000.
http://www.kernel-machines.org/papers/upload_11483_ICARCV2000-4.ps
and
H.G. Chew, R.E. Bogner, and C.C. Lim. Dual nu-support vector machine
with error rate and training size biasing. In International Conference on
Acoustics, Speech and Signal Processing, ICASSP 2001, USA, 2001.
http://www.kernel-machines.org/papers/upload_11513_ICASSP2001-nu-svm-1.ps
Cheers,
Hong-Gunn
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