I have a question about data mining. I have a dataset of 70 instances with
14 features that belong to 4 classes. As the number of each class is not
enough to obtain a good accuracy using some classifiers( svm, rna, knn) I
need to "oversampling" the number of instances of each class.

I have heard that there is a method to do this. It consists in generating
these new instances as follows:

new_instance <---- original_instance + u(epsilon)

 U(epsilon) is a uniform number in the range [-epsilon,epsilon] and this
number is applied to each feature of the dataset to obtain a new instance
without modified the original class.

Anybody has used this method to "oversampling" his data? Anybody has more
information about it?

Thanks in advance!

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